Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods

Abstract

Early diagnosis and precise treatment of gastrointestinal (GI) diseases are crucial for reducing mortality and improving quality of life. In this context, the detection and classification of abnormalities in endoscopic images is an important support for specialists during the diagnostic process. In this study, an innovative deep learning approach for the segmentation and classification of pathological regions in the GI system is presented. In the first phase of the study, a novel segmentation network called GISegNet was developed. GISegNet is a deep learning-based architecture tailored for accurate detection of anomalies in the GI system. Experiments conducted on the Kvasir dataset showed that GISegNet achieved excellent results on performance metrics such as Jaccard and Dice coefficients and outperformed other segmentation models with a higher accuracy rate (93.16%). In the second phase, a hybrid deep learning method was proposed for classifying anomalies in the GI system. The features extracted from the transformer-based models were fused and optimized using the Minimum Redundancy Maximum Relevance (mRMR) algorithm. The classification process was performed using Support Vector Machines (SVM). As a result of feature fusion and selection, the second model, which combined features from DeiT and ViT models, achieved the best performance with an accuracy rate of 95.2%. By selecting a subset of 300 features optimized by the mRMR algorithm, the accuracy (95.3%) was maintained while optimizing the computational cost. These results show that the proposed deep learning approaches can serve as reliable tools for the detection and classification of diseases of the GI system.