Classifying Liver Fibrosis through the Utilization of Transfer Learning and FCNET Classifiers

Classifying Liver Fibrosis through the Utilization of Transfer Learning and FCNET Classifiers

Publication Date : 2024-04-14
Author(s) :

S. Karthick

           
Article Name :

Classifying Liver Fibrosis through the Utilization of Transfer Learning and FCNET Classifiers

Abstract :

Abdominal Radiology is used for monitoring patients with liver diseases and to measure severity of liver fibrosis by using Ultrasound (US) images which is obtained by scanning. Scanning uses high frequency sound waves to take images of the affected human organs. Since liver is situated deep into the body the signal get weaker each time it passes through other organs which makes it difficult for diagnosis to overcome this difficult transfer learning technique is proposed in this system. The first step is to remove unwanted disturbances from the US images which is accomplished by pre-processing steps. To ensure the quality of the input images they undergo resizing of resolution, adjusting variations in brightness and contrast. The goal of pre-processing technique is to enhance the quality, uniformity, and relevance of the input images, making them suitable for feature extraction and subsequent classification of liver fibrosis stages. The next step is to apply the Expectation Maximum (EM) algorithm based on Region of Interest (ROI), which is a clustering technique used for image segmentation. The EM algorithm iteratively assigns pixels to different clusters based on their intensities and probabilistic models, aiming to identify distinct regions. The feature extraction process is carried out by Fully Connected Convolutional Neural Network (FCNET) models which enable extracting high level representations from original images and transfer learning classification is used for classification of fibrosis stages with improved accuracy.

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