A UNIFIED PARADIGM OF CLASSIFYING GI TRACT DISEASES IN ENDOSCOPY IMAGES USING MULTIPLE FEATURES FUSION

Abstract

The automatic identification of Gastrointestinal (GI) tract diseases in endoscopy images has been associated with the domain of medical imaging and computer vision. Its classification has various challenges, including color, low contrast, lesion shape, and complex background. A Deep features-based method for the classification of gastrointestinal disease is implemented in this article. The method suggested involves four significant steps: preprocessing, extraction of handcrafted, and deep Convolutional neural network features (Deep CNN), selection of solid features, fusion, and classification. 3D-Median filtering in the preprocessing stage increases the lesion contrast. The second stage extracts the features centered on the shape. The extracted features are of three types: HOG features, ResNet50, and Xception. Principal Component Analysis (PCA) is chosen to select extracted features, combined by concatenating them in a single array. A support vector system eventually categorizes fused features into multiple classes. The Kvasir dataset is used for the proposed model. The SVM has outstanding efficiency reached 96.6 percent, showing the proposed system's robustness.