Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture

Abstract

Colorectal cancer is one of the most common malignancies and the leading cause of cancer death worldwide. Wireless capsule endoscopy is currently the most frequent method for detecting precancerous digestive diseases. Thus, precise and early polyps segmentation has significant clinical value in reducing the probability of cancer development. However, the manual examination is a time-consuming and tedious task for doctors. Therefore, scientists have proposed many computational techniques to automatically segment the anomalies from endoscopic images. In this paper, we present an end-to-end 2D attention residual U-Net architecture (AttResU-Net), which concurrently integrates the attention mechanism and residual units into U-Net for further polyp and bleeding segmentation performance enhancement. To reduce outside areas in an input image while emphasizing salient features, AttResU-Net inserts a sequence of attention units among related downsampling and upsampling steps. On the other hand, the residual block propagates information across layers, allowing for the construction of a deeper neural network capable of solving the vanishing gradient issue in each encoder. This improves the channel interdependencies while lowering the computational cost. Multiple publicly available datasets were employed in this work, to evaluate and verify the proposed method. Our highest-performing model was AttResU-Net, on the MICCAI 2017 WCE dataset, which achieved an accuracy of 99.16%, a Dice coefficient of 94.91%, and a Jaccard index of 90.32%. The experiment findings show that the proposed AttResU-Net overcomes its baselines and provides performance comparable to existing polyp segmentation approaches.

Artificial intelligence in capsule endoscopy: development status and future expectations

“…Since then, 12 deep learning applications were used for polyp and tumour detection [63][64][65][66][67][68][69][70][71][72][73][74] . More recently, a study by Lafraxo et al in 2023 proposed an innovative model using CNN (Resnet50), where they achieved an accuracy of 99.16% on the MICCAI 2017 WCE dataset [73] . In 2022, the research conducted by Piccirelli et al investigating the diagnostic accuracy of Express View of IntroMedic achieved a 97% sensitivity and 100% specificity [75] .…”

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…Deep Learning (DL): The most widely used deep learning approach for image classification and segmentation is the Convolutional Neural Network (CNN). Several CNN models, such as AlexNet [ 95 ], LeNet [ 127 ], Fully Convolutional Neural Network (FCN) [ 27 , 128 ], Visual Geometry Group Network (VGGNet) [ 129 ], Residual Network (Resnet-50) [ 130 , 131 , 132 ], Res2Net101 [ 133 ], Inception-Resnet-V2 [ 134 , 135 ], AttResU-Net [ 136 ], MobileNet [ 42 ], DenseNet [ 24 ], Region-based Convolutional Neural Networks (R-CNN) [ 137 ], Convolutional Recurrent Neural Network (CRNN) [ 34 ], U-Net [ 44 , 138 ], SegNet [ 46 ], and custom CNNs [ 41 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 ], have been utilized in a number of studies for the classification or segmentation or combined classification and segmentation of bleeding in CE images. The study in [ 27 ] presented an FCN model for an automatic blood region segmentation system.…”

“…Res2Net101 [133], Inception-Resnet-V2 [134,135], AttResU-Net [136], MobileNet [42], DenseNet [24], Region-based Convolutional Neural Networks (R-CNN) [137], Convolutional Recurrent Neural Network (CRNN) [34], U-Net [44,138], SegNet [46], and custom CNNs [41,[139][140][141][142][143][144][145][146][147][148][149][150][151][152][153], have been utilized in a number of studies for the classification or segmentation or combined classification and segmentation of bleeding in CE images. The study in [27] presented an FCN model for an automatic blood region segmentation system.…”