A Deep Learning Approach for Classification and Prediction of Cirrhosis Liver: Non Alcoholic Fatty Liver Disease (NAFLD)

The early stage liver diseases prediction is an important health related research and using this kind of research easily can predict the diseases and take the remedies. The liver diseases are classified into different types such as liver cancer, liver tumor, fatty liver, hepatitis, cirrhosis etc. The early prediction of cirrhosis and earlier stages of liver failure reduce the risk. In this work proposed a new deep learning approach for prediction and classification of cirrhosis liver based on the non alcoholic fatty liver disease. The proposed work consists of different features, deep neural network and Spearman's rank correlation. The 52 features such as gray level co-occurrence matrix (GLCM) texture features, gradient co-occurrence matrix (GLGCM) texture features are used for classification and prediction. The deep neural network (DNN) used to feature prediction and classification. Based on the different features the various types of the classifications are performed. The Spearman's rank correlation used to predict the rank correlation using different layers of the DNN network. The experiment of the proposed work is performed using MRI images and datasets. The predicted dataset is evaluated using sensitivity, specificity, accuracy and precision. The predicted results are compared with existing dominated methods and it shows better results in terms of comparison parameters.