Gastrointestinal Tract Infections Classification Using Deep Learning

Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, micro-scopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the diagnosis and classification of GI tract diseases. A novel framework is pre-sented where preprocessing (LAB color space) is performed first; then local binary patterns (LBP) or texture and deep learning (inceptionNet, ResNet50, and VGG-16) features are fused serially to improve the prediction of the abnormalities in the GI tract. Additionally, principal component analysis (PCA), entropy, and minimum redundancy and maximum relevance (mRMR) feature selection methods were analyzed to acquire the optimized characteris-tics, and various classifiers were trained using the fused features. Open-source color image datasets (KVASIR, NERTHUS, and stomach ULCER) were used for performance evaluation. The study revealed that the subspace dis-criminant classifier provided an efficient result with 95.02% accuracy on the KVASIR dataset, which proved to be better than the existing state-of-the-art approaches.

Automated summary

The study proposed a CADx for diagnosing and classifying GI tract diseases, using a novel framework that preprocesses in LAB color space and then fuses LBP or deep learning features. The research revealed that the subspace discriminant classifier achieved an accuracy of 95.02% on the KVASIR dataset, surpassing existing state-of-the-art approaches.