Detection and classification of gastrointestinal disease using convolutional neural network and SVM

Gastrointestinal tract is a series of hollow organs connected in a long tube twisting from the mouth to the anus. Recovery of gastrointestinal diseased patients depends on the early diagnosis of the disease and proper treatment. In recent years, the diagnosis of gastrointestinal tract diseases using endoscopic image classification has become an active area of research in the biomedical domain. However, previous studies show that there is a need for improvement as some classes are more difficult to identify than others. In this study, we propose a concatenated neural network model by concatenating the extracted features of VGGNet and InceptionNet networks to develop a gastrointestinal disease diagnosis model. The deep convolutional neural networks VGGNet and InceptionNet are trained and used to extract features from the given endoscopic images. These extracted features are then concatenated and classified using machine learning classification techniques (Softmax, k-Nearest Neighbor, Random Forest, and Support Vector Machine). Among these techniques, support vector machine (SVM) achieved the best performance compared to others, using the available standard dataset. The proposed model achieves a classification accuracy of 98% and Matthews's Correlation Coefficient of 97.8%, which is a significant improvement over previous techniques and other neural network architectures.