Ensemble Deep Learning For Colorectal Cancer Detection With Chatbot Integration For User Engagement

Abstract

Colorectal cancer (CRC) has emerged as a significant public health concern in India, representing one of the most prevalent types of cancer in the country, with approximately 1.14 lakh new cases reported annually according to the National Cancer Registry Programme. This alarming trend highlights the urgent need for effective early detection and treatment strategies. Historically, traditional diagnostic methods for colorectal cancer, such as colonoscopy and fecal occult blood tests, have been the mainstay of detection; however, these procedures often come with limitations, including invasiveness, discomfort, and high costs, which can deter patients from seeking timely medical intervention. Additionally, the lack of awareness and early screening programs has resulted in many cases being diagnosed at advanced stages, leading to lower survival rates. The traditional approach relies heavily on manual processes and subjective interpretation, which can lead to variability in diagnosis and outcomes. In contrast, integrating machine learning and artificial intelligence (AI) into colorectal cancer detection systems presents a promising solution to these challenges. By employing ensemble deep learning techniques, we can enhance diagnostic accuracy and reliability through the analysis of vast amounts of medical data, including imaging and genetic information. Moreover, integrating a chatbot for user engagement can facilitate patient education, symptom assessment, and appointment scheduling, thereby improving accessibility to healthcare services.