Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network

2020 ◽  
Vol 92 (1) ◽  
pp. 144-151.e1 ◽  
Author(s):  
Hiroaki Saito ◽  
Tomonori Aoki ◽  
Kazuharu Aoyama ◽  
Yusuke Kato ◽  
Akiyoshi Tsuboi ◽  
...  

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


2019 ◽  
Vol 35 (7) ◽  
pp. 1196-1200 ◽  
Author(s):  
Tomonori Aoki ◽  
Atsuo Yamada ◽  
Yusuke Kato ◽  
Hiroaki Saito ◽  
Akiyoshi Tsuboi ◽  
...  

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