scholarly journals Prelimenary results of red lesion segmentation in WCE images

2021 ◽  
Vol 297 ◽  
pp. 01060
Author(s):  
Said Charfi ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani ◽  
Ilyas El Jaafari
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Imaging Technique ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Gastrointestinal Illness ◽  
Lesion Segmentation ◽  
Entire Small Bowel ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.

scholarly journals Multi-scale Features Fusion for the Detection of Tiny Bleeding in Wireless Capsule Endoscopy Images

2022 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Feng Lu ◽  
Wei Li ◽  
Song Lin ◽  
Chengwangli Peng ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
State Of The Art ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Feature Representation ◽  
Common Symptom ◽  
Content Type ◽  
Multi Scale ◽  
Wireless Capsule

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

scholarly journals An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1617
Author(s):  
Ioannis Intzes ◽  
Hongying Meng ◽  
John Cosmas
Keyword(s):  
Power Consumption ◽  
Capsule Endoscopy ◽  
State Of The Art ◽  
Logic Gates ◽  
Low Complexity ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Huffman Coding ◽  
Medical Diagnoses ◽  
Wireless Capsule

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.

scholarly journals Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132850-132859 ◽  
Author(s):  
Muhammad Attique Khan ◽  
Seifedine Kadry ◽  
Majed Alhaisoni ◽  
Yunyoung Nam ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Features Selection ◽  
Computer Aided ◽  
Wireless Capsule

scholarly journals Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sen Wang ◽  
Yuxiang Xing ◽  
Li Zhang ◽  
Hewei Gao ◽  
Hao Zhang
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Recognition Task ◽  
Wireless Capsule Endoscopy ◽  
Test Accuracy ◽  
Learning Networks ◽  
Deep Convolutional Neural Networks ◽  
Deep Cnn ◽  
Wireless Capsule ◽  
Better Than

Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.

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