Supervised Learning-based Cancer Detection

The segmentation, detection and extraction of the infected tumor from Magnetic Resonance Imaging (MRI) images are key concerns for radiologists or clinical experts. But it is tedious time consuming its accuracy depends on their experience only. This paper suggest a new methodology recognition, classification different types cancer cells both MRI RGB (Red, Green, Blue) performed using supervised learning, Convolutional Neural Network (CNN) morphological operations. In this methodology, CNN used to classify semantic segmentation segment cells. system trained pixel labeled ground truth where every image as cancerous non-cancerous. with 70%images validated tested rest 30%. Finally, segmented region extracted percentage area calculated. research examined MATLAB platform cell BreCaHAD dataset breast cancer, SN-AM Dataset leukemia, Lung Colon Cancer Histopathological Images lung Brain Tumor Detection brain cancer.