Predicting The Correlation of Colorectal Lymphoma Using Convolution Neural Network Approach

Abstract

The Third most prevalent cause of cancer death in the world is colorectal lymphomas (CL)The lymphomas Volume is usually estimated using Magnetic Resonance imaging (MRI) which analyzes the mutation during medical diagnosis in advanced stages The first stage of work deals with Automated colorectal volume calculation using 3D MRI Images in which feature extraction by Iterative Multi-linear component analysis followed by CNN-based Multiscale phase level set segmentation process. Finally, a 3D simulation of the lymphoma of the colon is accomplished using the logical frustum model used for medical data rendering The second stage of research work deals with segmentation and classification challenges for normal and abnormal lymph nodes. The segmentation performed by Semi-Supervised Fuzzy Logic clustering Algorithm and scale-down bee herd optimization approach used to increase the classifier detection rate followed by Deep residual Boltzmann Convolution neural network for classification