Deep Learning based Approach for Efficient Segmentation and Classification using VGGNet 16 for Tissue Analysis to Predict Colorectal Cancer

Abstract

The pivotal strategy in a huge amount of image processing applications are to consider the significant features from the image data. In this, the interpretation, understanding, and description of the scene will be offered through machine. Image-dependent machine and deep learning mechanisms has shown expert-level accuracy recently in the classification of medical image. The images of tissue were segmented and classified to detect the tumor. In this paper, the effective technique for the segmentation and classification of tissue analysis image for the detection of colorectal tumor types by means of Microsatellite instability mutation status (MSImut) and microsatellite stable (MSS) data types were presented. In this study, K-Means based Morphological segmentation and deep learning based VGGNet 16 classification is carried out to recognize the outcomes of colorectal cancer depending on the tissue analysis samples. Initially, the input image is pre-processed in order to remove excess noise present in the image. K-Means with morphological segmentation technique is applied for the execution of segmentation process effectively. Deep learning based VGG NET CNN was an algorithm that are well-established and is employed for classification. The performance analysis is carried out and the outcomes are compared with existing techniques to prove the effectiveness of proposed method.