COLON CANCER IDENTIFICATION USING CNN INTEGRATED PARTICLE SWARM OPTIMIZATION

The Colon cancer prompts to more number of death as of late. The diagnosis of colon cancer as early is protected to treat the patient. To distinguish and treat this type of malignancy, Colonoscopy is applied normally. Several risk prediction models for colon cancer have been created and approved in various populations but colon cancer effecting the young adults. In this research, we projected a Supervised Learning Technique for detecting colon cancer in high dimensional information. One of the most important and very popular tool for performing the machine learning tasks that Includes novelty detection, classification or regression is Particle Swarm Optimization . PSO algorithm simulates animal’s social behaviour , including insects , herds , birds and fishes.A more precise classification of medical data can effectively predict the development of colon cancer, according to the findings of numerous research Rapid technical breakthroughs in the fields of image processing and machine learning have resulted in the development of a slew of quick computer-assisted diagnostic methods.The proposed system utilizes Modified PSO algorithm to locate colon cancer with higher precision rate.