A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve the learning capability of deep models and lessen the number of parameters. Besides, this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase. The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm. These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier. The experiment worked on the HyperKvasir dataset, which consists of 23 stomach-infected classes. At last, the proposedmethod obtained an improved accuracy of 93.90% on this dataset. Comparison is also conducted with some recent techniques and shows that the proposed method's accuracy is improved.