Snake Optimization with deep learning enabled disease detection model for colorectal cancer

Colorectal cancer (CC) is prevalently studied carcinoma and it grows in the colon area of huge intestine. Histopathologist Usually Examine the colon biopsy during surgery or colonoscopy. Initial recognition of CC is useful for maintaining the model of accumulated cancerous cells. In medicinal practices, histopathological study of tissue specimens usually occurs in a traditional method but automatic tools which utilize Artificial Intelligence (AI) systems create effectual outcomes in disease detection efficiency. Deep learning (DL)techniques are demonstrated to generate remarkable outcomes on histopathology images in several studies. This study presents a Snake Optimization with Deep Learning Enabled Disease Detection Model for Colorectal Cancer (SODL-DDCC). The presented SODL-DDCC technique concentrates on the identification of CC on histopathological images. In the preliminary stage, the presented SODL-DDCC technique executes bilateral filtering (BF) approach to remove noise. In addition, the presented SODL-DDCC technique exploits Inception v3 as a feature extracting model with SO algorithm as a hyperparameter maximizing process. For CC classification, the graph convolution network (GCN) model is exploited. The investigation outcome evaluation of the SODL-DDCC approach is evaluated on standard dataset and the outputs are evaluated under distinct features. The empirical outputs highlighted the enhancements of the SODL-DDCC over current approaches.