Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

Abstract

Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique. The proposed AAI-CCDC technique focuses on the examination of histopathological images to diagnose colorectal cancer. Initially, AAI-CCDC technique performs pre-processing in three levels such as gray scale transformation, Median Filtering (MF)-based noise removal, and contrast improvement. In addition, Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature vectors. Furthermore, Glowworm Swarm Optimization (GSO) with Stacked Gated Recurrent Unit (SGRU) model is used for the detection and classification of colorectal cancer. The proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.