Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy

2019 ◽  
Author(s):  
Yejin Jeon ◽  
Eunbyul Cho ◽  
Sehwa Moon ◽  
Seung-Hoon Chae ◽  
Hae Young Jo ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Deep Convolutional Neural Network ◽  
Wireless Capsule

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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