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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]

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]

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]

Colorectal cancer is one of the leading causes of cancer death worldwide. It can appear in different forms depending on its location, and in most cases, these are tumors called polyps. A diagnostic model which is built can aid doctors but can not replace them. Thus, automated diagnostic technics to detect symptoms of illness or abnormalities in video colonoscopy or wireless capsule endoscopy (WCE) are adopted as an excellent enhancement technique for doctors. In this work, a new computer-assisted diagnosis method for polyp detection is proposed. After a preprocessing step, a fusion of two deep neural networks (DNNs), which were pre-trained on millions of labeled natural images (ImageNet), are fine-tuned and used to extract deep features to perform the polyp detection. The fine-tuning is employed using the Kvasir-seg dataset images. Moreover, the weights of the initial layers of the networks used in this work are frozen. Finally, we concatenated the fully connected outputs of the fine-tuned models to perform the binary classification. The proposed method achieved 0.919, 0.897, and 0.907 on the CVC-ClinicDB dataset, and 0.876, 0.910, and 0.893 on the ETIS-LaribPolypDB dataset in terms of precision, recall, and F-measure metrics, respectively. The results obtained are satisfactory when compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]