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Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal. [ABSTRACT FROM AUTHOR]

A water body is a common object in remote sensing images and high-quality water body extraction is important for some further applications. With the development of deep learning (DL) in recent years, semantic segmentation technology based on deep convolution neural network (DCNN) brings a new way for automatic and high-quality body extraction from remote sensing images. Although several methods have been proposed, there exist two major problems in water body extraction, especially for high resolution remote sensing images. One is that it is difficult to effectively detect both large and small water bodies simultaneously and accurately predict the edge position of water bodies with DCNN-based methods, and the other is that DL methods need a large number of labeled samples which are often insufficient in practical application. In this paper, a novel SFnet-DA network based on the domain adaptation (DA) embedding selective self-attention (SSA) mechanism and multi-scale feature fusion (MFF) module is proposed to deal with these problems. Specially, the SSA mechanism is used to increase or decrease the space detail and semantic information, respectively, in the bottom-up branches of the network by selective feature enhancement, thus it can improve the detection capability of water bodies with drastic scale change and can prevent the prediction from being affected by other factors, such as roads and green algae. Furthermore, the MFF module is used to accurately acquire edge information by changing the number of the channel of advanced feature branches with a unique fusion method. To skip the labeling work, SFnet-DA reduces the difference in feature distribution between labeled and unlabeled datasets by building an adversarial relationship between the feature extractor and the domain classifier, so that the trained parameters of the labeled datasets can be directly used to predict the unlabeled images. Experimental results demonstrate that the proposed SFnet-DA has better performance on water body segmentation than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth's surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality. [ABSTRACT FROM AUTHOR]

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In healthcare sector, image classification is one of the crucial problems that impact the quality output from image processing domain. The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases. Magnetic Resonance Imaging (MRI) is one of the effective non-invasive strategies that generate a huge and distinct number of tissue contrasts in every imaging modality. This technique is commonly utilized by healthcare professionals for Brain Tumor (BT) diagnosis. With recent advancements in Machine Learning (ML) and Deep Learning (DL) models, it is possible to detect the tumor from images automatically, using a computer-aided design. The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images (DLBTDC-MRI). The proposed DLBTDC-MRI technique aims at detecting and classifying different stages of BT. The proposed DLBTDC-MRI technique involves median filtering technique to remove the noise and enhance the quality of MRI images. Besides, morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image. Moreover, a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors. Finally, Artificial Fish Swarm Optimization (AFSO) with Artificial Neural Network (ANN) model is utilized as a classifier to decide the presence of BT. In order to assess the enhanced BT classification performance of the proposed model, a comprehensive set of simulations was performed on benchmark dataset and the results were validated under several measures. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CXRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 × 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model's performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance. [ABSTRACT FROM AUTHOR]

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth's surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications. [ABSTRACT FROM AUTHOR]

Images acquired in low-light conditions suffer from a series of visual quality degradations, e.g., low visibility, degraded contrast, and intensive noise. These complicated degradations based on various contexts (e.g., noise in smooth regions, over-exposure in well-exposed regions and low contrast around edges) cast major challenges to the low-light image enhancement. Herein, we propose a new methodology by imposing a learnable guidance map from the signal and deep priors, making the deep neural network adaptively enhance low-light images in a region-dependent manner. The enhancement capability of the learnable guidance map is further exploited with the multi-scale dilated context collaboration, leading to contextually enriched feature representations extracted by the model with various receptive fields. Through assimilating the intrinsic perceptual information from the learned guidance map, richer and more realistic textures are generated. Extensive experiments on real low-light images demonstrate the effectiveness of our method, which delivers superior results quantitatively and qualitatively. The code is available at https://github.com/lingyzhu0101/GEMSC to facilitate future research. [ABSTRACT FROM AUTHOR]

Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images. [ABSTRACT FROM AUTHOR]

Combining the proposed task-driven X-ray image cropping scheme with deep detection algorithms, the integrated framework can further boost popular detection algorithms, with higher detection mAPs and efficiency. Recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in the emerging field of computational sensing and imaging. [Extracted from the article]