COLON CANCER IDENTIFICATION USING CNN INTEGRATED PARTICLE SWARM OPTIMIZATION

COLON CANCER IDENTIFICATION USING CNN INTEGRATED PARTICLE SWARM OPTIMIZATION

MADHAN.S , JANARTHANAN.R ,AMIRTHA.C , SINDHUKAVI.R

Department of Computer Science and Engineering - University College of Engineering Thirukkuvalai, Nagappattinam , Tamilnadu , India.

(A Constituent College of Anna University::Chennai and Approved by AICTE, New Delhi)

Abstract

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.

Keywords

Colon Cancer, Particle Swarm Optimization(PSO), Machine Learning , Convolutional Neural Network (CNN),Colon Cancer prediction.