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Peptic ulcers and stomach cancer are common conditions that impact the gastrointestinal (GI) system. Wireless capsule endoscopy (WCE) has emerged as a widely used, noninvasive technique for diagnosing these issues, providing valuable insights through the detailed imaging of the GI tract. Therefore, an early and accurate diagnosis of GI diseases is crucial for effective treatment. This paper introduces the Intelligent Learning Rate Controller (ILRC) mechanism that optimizes the training of deep learning (DL) models by adaptively adjusting the learning rate (LR) based on training progress. This helps improve convergence speed and reduce the risk of overfitting. The ILRC was applied to four DL models: EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2. These models were further enhanced using transfer learning, freezing layers, fine-tuning techniques, residual learning, and modern regularization methods. The models were evaluated on two datasets, the Kvasir-Capsule and KVASIR v2 datasets, which contain WCE images. The results demonstrated that the models, particularly when using ILRC, outperformed existing state-of-the-art methods in accuracy. On the Kvasir-Capsule dataset, the models achieved accuracies of up to 99.906%, and on the Kvasir-v2 dataset, they achieved up to 98.062%. This combination of techniques offers a robust solution for automating the detection of GI abnormalities in WCE images, significantly enhancing diagnostic efficiency and accuracy in clinical settings. [ABSTRACT FROM AUTHOR]

BACKGROUND: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field. OBJECTIVE: Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances. METHODS: In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features. RESULTS: The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness. CONCLUSIONS: The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively. [ABSTRACT FROM AUTHOR]

As an emerging technology that uses a pill‐sized camera to visualize images of the digestive tract. Wireless capsule endoscopy (WCE) presents several advantages, since it is far less invasive, does not need sedation and has less possible complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro‐intestinal diseases such as polyps, ulcers, crohns disease, and hemorrhages. Nevertheless, WCE videos produced after a test may consist of thousands of frames per patient that must be viewed by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. Hence, development of an automatic tool based on artificial intelligence might be very helpful. Moreover, most state‐of‐the‐art works aim at images classification (normal/abnormal) while ignoring diseases segmentation. Therefore, in this work a novel method based on Feature Pyramid Network model is presented. This approach aims at diseases segmentation from WCE images. In this model, modules to optimize and combine features were employed. Specifically, semantic and spatial features were mutually compensated by spatial attention and cross‐level global feature fusion modules. The proposed method testing F1‐score and mean intersection over union are 94.149% and 89.414%, respectively, in the MICCAI 2017 dataset. In the KID Atlas dataset, the method achieved a testing F1‐score and mean intersection over union of 94.557% and 90.416%, respectively. Through the performance analysis, the mean intersection over union in the MICCAI 2017 dataset is 20.414%, 18.484%, 11.444%, 8.794% more than existing approaches. Moreover, the proposed scheme surpassed the methods used for comparison by 29.986% and 9.416% in terms of mean intersection over union in KID Atlas dataset. These results indicate that the proposed approach is promising in diseases segmentation from WCE images. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images. [ABSTRACT FROM AUTHOR]

Deep learning techniques can accurately detect and grade abnormal findings on images from Wireless Capsule Endoscopy (WCE). However, the prediction accuracy of handcrafted or deep learning in red Lesion, polyp and ulcer diseases is still under investigation. Knowing the utility of an automatic method for abnormalities detection from WCE images and how helpful it might be for the physicians, we proposed a new methodology in approaching this field. In this paper, patches with fixed size are extracted from WCE images, then, encoded using linear projection and position embedding and passed through an embedding model in a forward pass. Moreover, triplet loss is employed to adjust the embeddings. Afterwards, the trained embedding model is exploited for classification. Two strategies are followed in the design of the embedding model namely; training from scratch and fine tuning. The presented scheme, attains satisfactory results in different datasets compared to existing approaches. The detection accuracy has reached 99.9% in some used datasets. [ABSTRACT FROM AUTHOR]

Background: Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. Methods: We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. Results: Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. Conclusions: The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (CE) has revolutionized gastrointestinal diagnostics, offering a non-invasive means to visualize and monitor the GI tract. This review traces the evolution of CE technology. Addressing the limitations of traditional white light (WL) CE, the paper explores non-WL technologies, integrating diverse sensing modalities and novel biomarkers to enhance diagnostic capabilities. Concluding with an assessment of Technology Readiness Levels, the paper emphasizes the transformative impact of non-WL colon CE devices on GI diagnostics, promising more precise, patient-centric, and accessible healthcare for GI disorders. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

Wireless capsule endoscopy (WCE) is a technology that uses a pill-sized camera to visualize images of the digestive tract. It presents several advantages, since it is far less invasive, does not require sedation and has less potential complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, Crohn's disease and hemorrhages. Nevertheless, WCE videos can contain thousands of images per patient that must be screened by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. In this paper, a Nouvel method based on Dense-UNet deep learning segmentation model is presented. This approach aims at red lesion, ulcer and polyp detection from WCE images. Then, we propose a modified residual attention network for images classification. The proposed methods training and validation accuracies are 97.57% and 92.70%, with training and validation intersection over union and dice coefficients of 75.31%, 71.29%, 83.50% and 80.66%, respectively, in the red lesion dataset. In the polyp dataset, the method achieved a training and validation accuracies of 98.26% and 92.33%, with training and validation intersection over union and dice coefficients of 92.09%, 95.62%, 80.13% and 87.14%, respectively. Besides, the proposed architecture reached an average accuracy of 99.30%, sensitivity, specificity and F1 score of 98.61%, 100% and 99.30%, respectively. These results demonstrate that the proposed approach is satisfactory in polyp and red lesion segmentation and ulcer detection. [ABSTRACT FROM AUTHOR]

The classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐related deaths. The complexities involved in resolving gastrointestinal tract (GIT) ailments arise from the need for elaborate methods to precisely identify the exact location of the problem. Therefore, doctors frequently use wireless capsule endoscopy to diagnose and treat GIT problems. This research aims to develop a robust framework using deep learning techniques to effectively classify GIT diseases for therapeutic purposes. A CNN based framework, in conjunction with the feature selection method, has been proposed to improve the classification rate. The proposed framework has been evaluated using various performance measures, including accuracy, recall, precision, F1 measure, mean absolute error, and mean squared error. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) has revolutionized the field of gastrointestinal examinations, being MedtronicTM WCE one of the most used in clinics. In those WCE videos, medical experts use RAPID READERTM tool to annotate findings in videos. However, the frame annotations are not available in an open format and, when exported, they have different resolutions and some annotated artefacts that make difficult their localization in the original videos. This difficult the use of WCE medical experts' annotations in the research of new computed-aid diagnostic (CAD) methods. In this paper, we propose a methodology to compare image similarities and evaluate it in a private MedtronicTM WCE SB3 video dataset to automatically identify the annotated frames in the videos. We used state-of-the-art pre-trained convolutional neural network (CNN) models, including MobileNet, InceptionResNetv2, ResNet50v2, VGG19, VGG16, ResNet101v2, ResNet152v2, and DenseNet121, as frame features extractors and compared them with the Euclidean distance. We evaluated the methodology performance on a private dataset consisting of 100 WCE videos, totalling 905 frames. The experimental results showed promising performance. The MobileNet model achieved an accuracy of 94% for identifying the first match, while the top 5, top 10, and top 20 matches were identified with accuracies of 94%, 94%, and 98%, respectively. The VGG16 and ResNet50v2 models also demonstrated strong performance, achieving accuracies ranging from 88% to 93% for various match positions. These results highlight the effectiveness of our proposed methodology in localizing target frames and even identifying similar frames very use useful for training data-driven models in CAD research. The code utilized in this experiment is available on the Github†. [ABSTRACT FROM AUTHOR]

Diagnosis of anomalies in the gastrointestinal tract is a current research area. Wireless capsule endoscopy (WCE) for the evaluation of this region is a preferred alternative technology to avoid the risks of traditional endoscopy and to provide a painless process. But this technology, which has many advantages, offers low frame density. This situation affects the quality of the data and causes to a decrease in the diagnostic accuracy rate. In this study, WCE endoscopy images obtained from KID Atlas Dataset 2 were used and a three-stage artificial intelligence-supported diagnostic process was developed for the detection of the inflammatory anomaly, vascular anomaly, polypoid anomaly and normal image categories in the gastrointestinal tract. For the first stage, critical points on the images were clarified using 5 different approaches. These improved images were classified with a region proposal-based object recognition algorithm and performance comparison was made according to the approaches used. In the second stage, the data augmentation technique was applied to the improved images that showed maximum performance in the first stage. Thus, a dataset with a balanced and sufficient number of images was created. In the third stage, this current dataset was classified with five different object recognition algorithms. However, the individual success of each algorithm is different. For this reason, the ensemble learning approach was used to obtain stable outputs for each category and to create a balanced detection process among the categories. Finally, a balanced and stable estimation function was provided between categories with this hybrid structure. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

In the last decades an innovative technique has emerged in clinical gastroenterology as a compelling alternative to the traditional wired endoscopy, known as Wireless Capsule Endoscopy (WCE). Such cutting-edge application is able to investigate the gastrointestinal (GI) tract through a miniaturized, swallowable and biocompatible capsule, equipped with electronic components. This allows for the noninvasive measurement of biological data, that is then sent to an external receiving unit through a wireless link. This systematic review prepared according to PRISMA guidelines focuses on the main technological advances of data transmission from the in-body ingestible capsule to an external receiver. A total of 142 studies were screened from a comprehensive literature search, performed in Scopus, Science Direct, and IEEE Xplore database. A final number of 47 met the inclusion criteria and were included in the review. The results highlight innovative technologies to optimize the wireless link efficacy and safety to an external receiver. High gain, wideband, omnidirectional radiation pattern, and low levels of specific absorption rate (SAR) are of crucial importance. Despite the capsule telemetry design being rather advanced, the bulk of the existing studies focus on the transmission unit design, rather than the receiving one. Moreover, comprehensive numerical studies on realistic human body models are lacking.

Wireless capsule endoscopy (WCE) is a huge step forward in diagnosing problems in the digestive system because it can take pictures without hurting the patient. As part of this study, we thoroughly test anomaly detection models that were created with the specific goal of finding WCE disease. The Single Shot Multibox Detector (SSD), EfficientDet, Faster RCNN, RetinaNet, yolov3, Real-Time Multi-Detector (RTMDet), and semi-supervised learning detection models were tested on a translated MS COCO dataset from the Kvasir-Capsule repository. The dataset has a variety of gastrointestinal abnormalities. We used three main evaluation methods: normalized confusion matrices for class-specific accuracy, macro and weighted averages(precision, recall, and F-score). We compared normal and semi-supervised versions of models like Faster RCNN to find out what the benefits of semi-supervised methods are. The models that were tested showed that RTMDet-S and RTMDet-Tiny were the most accurate in their class, finding complex diseases with over 99% accuracy. With an precision, recall, and F-score of 99.67%, SSD300 also had the best total result among statistics measures. Along with that, YOLOv3 was the model that worked the best in terms of operating economy. It achieved a frame rate of 92.1 frames per second while using only 376MiB of memory. There were big improvements in sensitivity and specificity to 98.70% and 99.84% for semi-supervised models like Soft-teacher (Faster RCNN + ResNet).The purpose of this study is to assist researchers and clinicians in improving their diagnostic abilities by providing highly informative information on the most effective object detection models that can be used for the identification of WCE pathology.

Peptic ulcers and stomach cancer are common conditions that impact the gastrointestinal (GI) system. Wireless capsule endoscopy (WCE) has emerged as a widely used, noninvasive technique for diagnosing these issues, providing valuable insights through the detailed imaging of the GI tract. Therefore, an early and accurate diagnosis of GI diseases is crucial for effective treatment. This paper introduces the Intelligent Learning Rate Controller (ILRC) mechanism that optimizes the training of deep learning (DL) models by adaptively adjusting the learning rate (LR) based on training progress. This helps improve convergence speed and reduce the risk of overfitting. The ILRC was applied to four DL models: EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2. These models were further enhanced using transfer learning, freezing layers, fine-tuning techniques, residual learning, and modern regularization methods. The models were evaluated on two datasets, the Kvasir-Capsule and KVASIR v2 datasets, which contain WCE images. The results demonstrated that the models, particularly when using ILRC, outperformed existing state-of-the-art methods in accuracy. On the Kvasir-Capsule dataset, the models achieved accuracies of up to 99.906%, and on the Kvasir-v2 dataset, they achieved up to 98.062%. This combination of techniques offers a robust solution for automating the detection of GI abnormalities in WCE images, significantly enhancing diagnostic efficiency and accuracy in clinical settings.

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%. [ABSTRACT FROM AUTHOR]

The colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among the most prevalent type of cancer. The early diagnosis of the CRC can be done by periodic examination of the colon and rectum for innocuous tissue abnormality called polyp as it has the potential to evolve as malignant in future. The CRC diagnosis using wireless capsule endoscopy requires the dedicated commitment of the medical expert demanding significant time, focus and effort. The accuracy of manual analysis in identifying polyps is extensively reliant on the cognitive condition of the physician, thus emphasizing the requirement for automatic polyp identification. The artificial intelligence integrated computer‐aided diagnosis system could assist the clinician in better diagnosis, thereby reducing the miss‐rates of polyps. In our proposed study, we developed an application program interface to aid the clinician in automatic segmentation of the polyp and evaluation of its dimension by manual placement of four landmarks on the predicted polyp. The segmentation is performed by the proposed light weight Padded U‐Net for the effective polyp segmentation in the colorectal images. We trained and validated the Padded U‐Net with augmented images of Kvasir dataset and calculated the performance parameters. In order to facilitate image augmentation, a graphical user interface called Augment Tree was developed, which incorporates 92 augmentation techniques. The accuracy, recall, precision, IoU, F1‐score, loss achieved during validation of Padded U‐Net were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% and 0.080% respectively. We demonstrated that accuracy was improved and loss was reduced when the model was trained with augmented images rather than only the limited original dataset images. On comparison of our Padded U‐net architecture with recently developed architectures, our model attained optimal performance in all the metrics except accuracy in which it attained marginal performance to the highest value. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a non-invasive video technique used to investigate gastrointestinal diseases such as hemorrhage, ulcer, and polyp. Automatic detection systems that primarily use features derived from WCE images are being developed in order to bypass a difficult and time-consuming manual evaluation procedure. Bleeding is one the most prevalent anomalies in WCE images, This anomaly can be identified by its color features. In this paper, a computer-aided approach for detecting bleeding frames is proposed. The suggested system consists of three major phases: preprocessing, feature extraction using optimized convolutional neural network (CNN), and classification based on gated recurrent unit (GRU). We investigate our proposed CNN-GRU based methodology using a publicly available dataset called MICCAI 2017, and the results of the experiments demonstrate that our strategy is both efficient and robust, achieving high accuracy of 99.39% with considerable performance gains over the state-of-the-art. [ABSTRACT FROM AUTHOR]

Handling wireless capsule endoscopy (WCE) de-redundancy is a challenging task. This paper proposes a scheme, called SS-VCF-Der, to consider applying a flow field estimation between two successive WCE frames to WCE imaging motion analysis and then address the WCE de-redundancy problem based on the results of the motion analysis. To this end, we intend to exploit a self-supervised technique to learn interframe visual correspondence representations from large amounts of raw WCE videos without manual human supervision, and predict the flow field. Our key idea is to use the natural spatial-temporal coherence in color and cycle consistency in time in WCE videos as free supervisory signal to learn WCE visual correspondence relations from scratch. We call this procedure self-supervised visual correspondence flow learning (SS-VCF). At training time, we use three losses: forward-backward cycle-consistency loss, visual similarity loss, and color loss, to train and optimize model. At test time, we use the acquired representation to generate a flow field for analyzing pixel movement between two successive WCE frames. Furthermore, according to the resulting flow field estimation, we compute the motion intensity of motion fields between two successive frames, and use our proposed de-redundancy method, namely SS-VCF-MI, to select some frames as key ones with distinct scene changes in local neighborhood so as to achieve the purpose of de-redundancy. Extensive experiments on our collected WCE-2019-Video dataset show that our scheme can achieve a promising result, verifying its effectiveness on the visual correspondence representation and redundancy removal for WCE videos. [ABSTRACT FROM AUTHOR]

Abstract Background Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames. Methods In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy. Results 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study. Conclusion The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

Background: Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. Methods: We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model’s performance using WCE videos from 72 patients. Results: Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model’s performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model’s predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. Conclusions: The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model’s ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.

Background: A significant number of frames captured by the wireless capsule endoscopy are involved with varying amounts of bubbles. Whereas different studies have considered bubbles as nonuseful agents due to the fact that they reduce the visualization quality of the small intestine mucosa, this research aims to develop a practical way of assessing the rheological capability of the circular bubbles as a suggestion for future clinical diagnostic purposes. Methods: From the Kvasir-capsule endoscopy dataset, frames with varying levels of bubble engagements were chosen in two categories based on bubble size. Border reflections are present on the edges of round-shaped bubbles in their boundaries, and in the frequency domain, high-frequency bands correspond to these edges in the spatial domain. The first step is about high-pass filtering of border reflections using wavelet transform (WT) and Differential of Gaussian, and the second step is related to applying the Fast Circlet Transform (FCT) and the Hough transform as circle detection tools on extracted borders and evaluating the distribution and abundance of all bubbles with the variety of radii. Results: Border’s extraction using WT as a preprocessing approach makes it easier for circle detection tool for better concentration on high-frequency circular patterns. Consequently, applying FCT with predefined parameters can specify the variety and range of radius and the abundance for all bubbles in an image. The overall discrimination factor (ODF) of 15.01, and 7.1 showing distinct bubble distributions in the gastrointestinal (GI) tract. The discrimination in ODF from datasets 1–2 suggests a relationship between the rheological properties of bubbles and their coverage area plus their abundance, highlighting the WT and FCT performance in determining bubbles’ distributions for diagnostic objectives. Conclusion: The implementation of an object-oriented attitude in gastrointestinal analysis makes it intelligible for gastroenterologists to approximate the constituent features of intra-intestinal fluids. this can’t be evaluated until the bubbles are considered as non-useful agents. The obtained results from the datasets proved that the difference between the calculated ODF can be used as an indicator for the quality estimation of intraintestinal fluids’ rheological features like viscosity, which helps gastroenterologists evaluate the quality of patient digestion.

Wireless capsule endoscopy (WCE) has emerged as a valuable non-invasive technique for visualizing the entire gastrointestinal (GI) tract. However, manual evaluation of WCE videos is a time-consuming and costly process. In this study, we present a novel diagnostic assistant system that employs deep convolutional neural networks (DCNNs) to accelerate the evaluation process. Our primary objective is to achieve a high negative predictive value (NPV), which is essential for the efficient identification of normal frames. Six distinct DCNN models were developed and implemented with this objective in mind. The models were trained on a limited dataset encompassing common GI pathologies that reflect real clinical scenarios. Each DCNN architecture comprises a convolutional part derived from renowned pre-trained networks and a custom-designed classifier block optimized for high NPV and classification accuracy. Following a comprehensive assessment utilizing the 5-fold cross-validation approach, the VG_BFCG model was identified as the most effective, exhibiting an average test accuracy of 0.946 and an NPV of 0.983. Moreover, in the event of encountering novel pathologies not present in the training data, our models exhibited robustness in NPV, which is of great importance for practical applications. For example, the DN_BFCG model demonstrated consistent performance, with an NPV exceeding 0.99 across a range of new pathologies. This validates the reliability of our models in clinical settings. Our findings suggest that our developed DCNN architectures have the potential to enhance the efficiency and accuracy of WCE video analysis, which could transform the landscape of gastroenterological diagnostics.

Background: Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames. Methods: In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy. Results: 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study. Conclusion: The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy is a noninvasive wireless imaging method that has grown in popularity over the last several years. One of the efficient and effective ways for examining the gastrointestinal system is using WCE. It sends a huge number of images in a single examination cycle, making abnormality analysis and diagnosis extremely difficult and time-consuming. As a result, in this research, we provide the Expectation maximum (EM) algorithm, a revolutionary deep-learning-based segmentation approach for GI tract recognition in WCE images. DeepLap v3+ can extract a variety of features including colour, shape, and geometry, as well as SURF (speed-up robust features). Thus the Lenet 5 based classification can be made in the extracted images. The effectiveness of the performances is carried out on a publicly available Kvasir-V2 dataset, on which our proposed approach achieves 99.12% accuracy 98.79% of precision, 99.05% of recall and 98.49% of F1- score when compared to existing approaches. Effectiveness benefits are demonstrated over multiple current state-of-the-art competing techniques on all performance variables we evaluated, especially mean of Intersection Over Union (IoU), IoU for background, and IoU for the entire class. [ABSTRACT FROM AUTHOR]

The gold standard for diagnosing disorders of the small bowel is wireless capsule endoscopy (WCE). Capsule endoscopy appears to represent the future of effective diagnostic gastrointestinal (GI) endoscopy. As capsule endoscopy doesn't cause any discomfort, it stands a better chance of being adopted by patients than traditional colonoscopy and gastroscopy, making it a good option for detecting cancer or ulcerations. WCE can be helpful in obtaining images of the GI tract from the inside, but pinpointing exactly where the disease is located is still a major challenge. In this paper, reviewing of the studies dealing with the development of the endoscopy capsule and finding techniques and solutions to provide higher efficiency is presented. Also, the paper showed that the tendency to use artificial intelligence (AI) led to an increase in the accuracy of detecting diseases and a decrease in mistakes caused by physicians' lack of attention or fatigue while reading a video from a capsule, as well as the role of artificial intelligence in shortening the time it takes to read the video. When it comes to WCE, deep learning has shown remarkable success in detecting a wide variety of disorders. [ABSTRACT FROM AUTHOR]

An efficient tool to execute painless imaging and examine gastrointestinal tract illnesses of the intestine is also known as wireless capsule endoscopy (WCE). Performance, safety, tolerance, and efficacy are the several concerns that make adaptation challenging and wide applicability. In addition, to detect abnormalities, the great importance is the automatic analysis of the WCE dataset. These issues are resolved by numerous vision-based and computer-aided solutions. But, they want further enhancements and do not give the accuracy at the desired level. In order to solve these issues, this paper presents the detection and classification of WCE infected images by a deep neural network and utilizes a bleed image recognizer (BIR) that associates the MobileNetV2 design to classify the images of WCE infected. For the opening-level evaluation, the BIR uses the MobileNetV2 model for its minimum computation power necessity, and then the outcome is sent to the CNN for more processing. Then, Bi-LSTM with an attention mechanism is used to improve the performance level of the model. Hybrid attention Bi-LSTM design yields more accurate classification outcomes. The proposed scheme is implemented in the Python platform and the performance is evaluated by Cohen's kappa, F1-score, recall, accuracy, and precision. The implementation outcomes show that the introduced scheme achieved maximum accuracy of 0.996 with data augmentation with the dataset of WCE images which provided higher outcomes than the others. [ABSTRACT FROM AUTHOR]

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a small transparent thin film antenna for a wireless capsule endoscopy system. The transparent thin film antenna is needed to provide a clear 360° broad-sight view of the wireless capsule system in the future. Furthermore, the transparent thin film is critical for performing the dual-polarized antenna operating at 2.45 GHz. The proposed transparent thin film uses the nano-alignment process to further achieve low resistivity from 3.78 × 10−4 Ω-cm to 9.14 × 10−5 Ω-cm and improves the transparency by over 70%. The nano-alignment process includes periodic electrodes with AC signals that can effectively rearrange the nano-material into an ordered arrangement, enhancing the thin film's microwave characteristics. Due to applying to the capsule endoscopy system, the ability of water resistance is also considered in this design. Therefore, the O2 plasma treatment is used to improve the water contact angle from 76° to 31°, measured on the surface of the thin film. The proposed transparent antenna is designed to have a center frequency of 2.45 GHz, a bandwidth of 855 MHz, and an antenna gain of −26.3 dBi, and is helpful for capsule endoscopy systems. [ABSTRACT FROM AUTHOR]

Deep Learning has contributed significantly to the advances made in the fields of Medical Imaging and Computer Aided Diagnosis (CAD). Although a variety of Deep Learning (DL) models exist for the purposes of image classification in the medical domain, more analysis needs to be conducted on their decision-making processes. For this reason, several novel Explainable AI (XAI) techniques have been proposed in recent years to better understand DL models. Currently, medical professionals rely on visual inspections to diagnose potential diseases in endoscopic imaging in the preliminary stages. However, we believe that the use of automated systems can enhance both the efficiency for such diagnoses. The aim of this study is to increase the reliability of model predictions within the field of endoscopic imaging by implementing several transfer learning models on a balanced subset of Kvasir-capsule, a Wireless Capsule Endoscopy imaging dataset. This subset includes the top 9 classes of the dataset for training and testing. The results obtained were an F1-score of 97% ±1% for the Vision Transformer model, although other models such as MobileNetv3Large and ResNet152v2 were also able to achieve F1-scores of over 90%. These are currently the highest-reported metrics on this data, improving upon prior studies done on the same dataset. The heatmaps of several XAI techniques, including GradCAM, GradCAM++, LayersCAM, LIME, and SHAP have been presented in image form and evaluated according to their highlighted regions of importance. This is in an effort to better understand the decisions of the top-performing DL models and look beyond their black-box nature.

Wireless capsule endoscopy is a fast-growing technology in healthcare systems. Due to using battery for powering the camera, light source, wireless communication, and other electronics, it has substantial limitations with the image quality, frame rate, and operating time. In this work, we propose a wireless passive video transmission system for capsule endoscopy, in which the power consumption is reduced by using analog camera sensor, and implementing an innovative radar technique for remote reading of the analog video signal using radio frequency backscattering. The power consumption of the capsule communication system tends to zero. The communication electronics system is minimized to a single Varactor diode with appropriate matching circuits and the image sensor power consumption is reduced by eliminating the camera sensor’s analog to digital converter. With these improvements the capsule system can operate for a longer period of time which enables the feasibility of continuous video streaming during the gastric tract screening. The design feasibility is demonstrated in a phantom experiment, and validated in an animal experiment for depths 6–11 cm using a bi-static radar system at 400 MHz, implemented using software defined radio platform.

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Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth–Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising. [ABSTRACT FROM AUTHOR]

Endoscopic investigation plays a critical role in the diagnosis of gastrointestinal (GI) diseases. Since 2001, Wireless Capsule Endoscopy (WCE) has been available for small bowel exploration and is in continuous development. Over the last decade, WCE has achieved impressive improvements in areas such as miniaturisation, image quality and battery life. As a result, WCE is currently a very useful alternative to wired enteroscopy in the investigation of various small bowel abnormalities and has the potential to become the leading screening technique for the entire gastrointestinal tract. However, commercial solutions still have several limitations, namely incomplete examination and limited diagnostic capacity. These deficiencies are related to technical issues, such as image quality, motion estimation and power consumption management. Computational methods, based on image processing and analysis, can help to overcome these challenges and reduce both the time required by reviewers and human interpretation errors. Research groups have proposed a series of methods including algorithms for locating the capsule or lesion, assessing intestinal motility and improving image quality. In this work, we provide a critical review of computational vision-based methods for WCE image analysis aimed at overcoming the technological challenges of capsules. This article also reviews several representative public datasets used to evaluate the performance of WCE techniques and methods. Finally, some promising solutions of computational methods based on the analysis of multiple-camera endoscopic images are presented. [ABSTRACT FROM AUTHOR]

This work provides a novel segmentation and classification of diseases from the GI tract using WCE images. First, the input WCE images are collected from the KID Atlas data set, and they are preprocessed using filtering and contrast enhancement techniques. Then, the modified U-Net with parameter tuning by the Deer Hunting with Distance-based Solution Update (DH-DSU) is adopted for performing the lesion or abnormality segmentation. From the segmented images, the feature extraction is employed by a set of approaches such as 'Gray Level Co-occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Canny edge detection'. These features are concatenated and given to the improved Deep Neural Network (DNN) with DH-DSU-based hidden neuron optimisation. This provides the classification outcome in terms of three diseases of the GI tract such as inflammatory, polypoid and ulcer. Through the performance analysis, MCC of DH-DSU-IDNN is 8.49%, 1.73%, 18.46% and 17.23% more than DHOA-IDNN, WOA-IDNN, GWO-IDNN and PSO-IDNN, respectively. At the end, the offered approach has been validated on collected data sets, and the achievement of findings is adequate when compared with the baseline approaches. [ABSTRACT FROM AUTHOR]

While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%.