Detection And Classification Of Tumour Using Image Processing And Machine Learning

Abstract

Tumours, or aberrant unregulated cell development in any body component, can put enormous pressure on the numerous nerves and blood vessels, causing irreversible damage to the body. The key to avoiding such compilations is early tumour diagnosis. Advanced machine learning and image processing techniques can be used to detect tumours. Image pre-processing, segmentation, and feature extraction are all stages of tumour identification. Pre-processing include applying multiple filters to the image and removing noise. Methods such as thresholding and region growth are used in segmentation. For the retrieved tumour, features such as contrast, skewness, and entropy are calculated. To identify the tumour as benign or malignant, various classifiers such as convolution
neural networks and nave bayes are used.