An efficient categorization of liver cirrhosis using convolution neural networks for health informatics

Abstract

Accurate categorization of cirrhosis liver image in ultrasound modality is of great importance in medical diagnosis and treatment. Health informatics is important as it provides quick predictions on diseases based on nearness of symptoms. Modelling solutions for the same on cloud using deep learning is the motivation of this paper. Here, we propose a deep learning model associated with correlation based feature selection method for cirrhosis image classification. We compare the results with three other conventional classifiers algorithms to improve the better classification accuracy. First in pre-processing stage, noises are eliminated from pathological scan images by using modified laplacian pyramid non-linear diffusion filter. From the pre-processed scan images, each cirrhosis region is obtained under the guidance of radiology or physicians. Then, after extracting the complete features of each patch by gray level, local binary pattern and scale invariant feature, a feature selection technique is applied to choice the predominant texture features for each classifier. Finally a convolution neural network is implemented to improve the performance of classifiers in terms of sensitivity, specificity and accuracy. Convolution neural network algorithm with two hidden layers gives more accuracy in classifying cirrhosis image with 98% sensitivity. Experiments are carried out with 990 cirrhosis image patches which demonstrates that our proposed deep learning classifier perform 100% well than original classifiers in terms of accuracy.