A New Method Based on Convolutional Neural Networks and Discrete Wavelet Transform for Detection, Classification and Tracking of Colon Polyps in Colonoscopy Videos

Abstract

In this study, a new method based on Convolutional Neural Network (CNN), Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is presented for polyp detection, classification and tracking during colonoscopy. The proposed method is constructed in 3 parts. 1) Detection of polyps with deep learning based Faster R-CNN for detection of polyps 2) Classification of detected polyps by CNN-DWT-SVM. 3) Tracking for polyps counting. The proposed method was trained and tested with the Colonoscopy Dataset, a public data set. In the first step of the method, polyp detection was carried out with pre-trained ResNet 50 CNN architecture with 92.6% precision. The regions identified in the second step of the method were classified for four classes adenoma, hyperplastic, lumen, serrated and 94.7% classification accuracy was obtained. With the proposed method, the detection sensitivity of Faster R-CNN was increased from 92.6% to 99.2%, and the accuracy of 95.4% was achieved by using DWT in the classification of polyp classes. In the classification process, 98% correct adenoma, 95% hyperplastic, 90% luminal intestine, 96% serrated polyp were reached. The proposed method reached an average of 94% MOTA in polyp tracking and was able to detect polyp frames with their classes with 99.2% precision.