A convolutional neural network approach for abnormality detection in Wireless Capsule Endoscopy

2017 ◽  
Author(s):  
Anjany Kumar Sekuboyina ◽  
Surya Teja Devarakonda ◽  
Chandra Sekhar Seelamantula
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Network Approach ◽  
Abnormality Detection ◽  
Neural Network Approach ◽  
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

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