ID: 3492451 VALIDATION OF A BINARY CLASSIFICATION MODEL USING A DEEP CONVOLUTIONAL NEURAL NETWORK FOR WIRELESS CAPSULE ENDOSCOPY

2021 ◽  
Vol 93 (6) ◽  
pp. AB201
Author(s):  
Sang Hoon Kim ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Ji Hyung Nam ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Binary Classification ◽  
Wireless Capsule Endoscopy ◽  
Deep Convolutional Neural Network ◽  
Classification Model ◽  
Wireless Capsule

scholarly journals Efficacy of a Comprehensive Binary Classification Model Using a Deep Convolutional Neural Network for Wireless Capsule Endoscopy

2020 ◽  
Author(s):  
Sang Hoon Kim ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Ji Hyung Nam ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Wireless Capsule Endoscopy ◽  
Classification Model ◽  
Test Results ◽  
Reading Methods ◽  
Bowel Diseases ◽  
Validation Set ◽  
Wireless Capsule

Abstract Manual reading of capsule endoscopy (CE) video is a time-consuming process in diagnosing small bowel diseases. Although many algorithms have been introduced, multi-diagnosis has not been sufficiently validated. They are promising but still premature to be used in clinical practice. Therefore, we developed a practical binary classification model and tested it with unseen cases.400,000 CE images were randomly selected from 84 cases. Among them, 240,000 were used to train an algorithm to categorize images binarily. The remaining images were utilized for validation and internal testing. The algorithm was externally tested with 256,591 unseen images.Diagnostic accuracy was 98.067% when the trained model was applied to the validation set. It was 97.946% when applied to images for internal testing. When the model was applied to a dataset provided by an independent hospital not participated during training, its accuracy was 85.470%. The area under the curve was 0.922.Our binary classification model showed excellent internal test results, and when tested in unseen external cases, misreadings were slightly increased while judging ‘insignificant’ images containing ambiguous substances. When we can get over this problem, CNN-based binary classification will become the most promising candidates for developing clinically ready computer-aided reading methods.

scholarly journals A Convolutional Neural Network Based Disease Diagnosis in Wireless Capsule Endoscopy Images

2019 ◽  
Vol 9 (1) ◽  
pp. 327-331
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Operating Characteristic ◽  
Color Space ◽  
Disease Diagnosis ◽  
Wireless Capsule Endoscopy ◽  
Classifier Design ◽  
Multiple Abnormalities ◽  
Wireless Capsule

In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert’s time to review the scan. In this research, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches and extract features pertaining to each block using a Convolutional neural network (CNN) to increase their generality while overcoming the drawbacks of manually crafted features. We intend to exploit the importance of color information for the task. Experiments are performed to determine the optimal color space components for feature extraction and classifier design. We obtained an area under receiver-operating-characteristic (ROC) curve of approximately 0.8 on a dataset containing multiple abnormalities

scholarly journals Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Kaiwen Qin ◽  
Jianmin Li ◽  
Yuxin Fang ◽  
Yuyuan Xu ◽  
Jiahao Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Convolution Neural Network ◽  
Research Progress ◽  
Gi Bleeding ◽  
Wireless Capsule

Abstract Background Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE. Methods A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. Results 16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99). Conclusion Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

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