Liver segmentation using Mnet for cirrhosis

Abstract

Liver biopsies for diagnosing cirrhosis, the last stage of chronic liver disease was analysed using imaging and it could able to determine the severity level of the disease. Liver lesions can be segmented for evaluation of tumor burden, therapeutic strategy, prognosis, and follow-up on the efficacy of therapy. Automatic technologies for malignancy identification and segmentation are preferable because manual segmentation is a laborious process that is prone to error. We thus propose an M-Net approach to cirrhosis of the liver segmentation. The liver datasets are collected initially as raw data, which, due to the existence of noise, must be normalized before the investigation can begin. With the help of the Bilateral Filter and the Wavelet Transform, we can get rid of the noise and improve the de-noised image. The Gabor Filter is used for feature extraction. Hybrid Genetic Algorithm (HGA) is used to pick the best feature subsets for classification. Ensemble Deep Convolutional Neural Network (EDCNN) method is applied to the classification process. MATLAB, a simulation program, is used to conduct the entire inquiry. In terms of accuracy, our system has outperformed the most sophisticated automatic techniques.