Publication Date: 2021/07/28
Abstract: Wireless capsule endoscopy (WCE) is proving to an extremely beneficial method for perceiving ailments inside of human gastric pathway. Through WCE Doctors identify the diverse irregularities like chronic diarrhoea, ulcer, bleeding, polyps, cancer/tumour in small intestine and Crohn's disease, any of it in the gastrointestinal tract that is invasive. It is a method that used a pill-size wireless camera to get visual data of the digestive tract of a human body. It is a method that helps doctor to see inside your intestine, an area which is normally difficult to reach to through standard endoscopy procedure or imaging methods. Scholars are finding new ways to progress and enhance the metrics of performance of WCE using models that detect these ailments at a quicker rate of improvement, on their own. With datasets produced from this dataset are imporving with better technology and imaging services, machine learning grants us an opportunity to diagonis and get better insights from this data. Utilizing these methods, we propose a system to train and predict 13 classes of anomalies inside the digestive tract. The proposed model has achieved an accuracy of 97.82%
Keywords: Wireless Capsule Endoscopy, Machine Learning, Digestive Tract
DOI: No DOI Available
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT21JUL950.pdf
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