Prediction of Lung and Colon Cancer Using Image Processing in Machine Learning

EasyChair Preprint 8257

Abstract

Medicine and healthcare have progressed dramatically over the last four decades. The true reasons of a range of infections were discovered during this time, new medical testing were invented, and new treatments were developed. Despite our achievements, diseases like cancer continue to affect us because we are still vulnerable to them. Cancer is indeed the second leading cause of death in the world, killing one out of every six people. Cancer is the leading cause of death worldwide, with the most common types being gastrointestinal and lung cancer. The first stage classifies the existence and absence of a tumor using endoscopic image global features, while the second stage uses CNN (deep convolutional network) segmentation. According to the findings, the framework can detect cancer cells rise to 96.33 %. This model will assist medical professionals in developing a fully automated and reliable system that can detect various types of lung as well as colon cancers.

Keyphrases: Random Forest Classifier, image processing, lung and colon cancer

@booklet{EasyChair:8257,
  author    = {Rajesh Rangan and G Srimugambigai},
  title     = {Prediction of Lung and Colon Cancer Using Image Processing in Machine Learning},
  howpublished = {EasyChair Preprint 8257},
  year      = {EasyChair, 2022}}