JoWUA

Abstract

This study focuses on classifying liver diseases using dynamic CT scan images and deep learning techniques. The primary objective is to develop accurate and efficient models for distinguishing between different liver disease categories. Three deep learning models, ResNet50, ResNet18, and AlexNet, are employed for three-class classification, including Hepatitis/cirrhosis, Hepatitis/Fatty liver, and Hepatitis/Wilson's Disease. The dataset comprises dynamic CT scan images of the liver, each manually segmented to identify lesions. To enhance model performance, the data is pre-processed by resizing, normalization, and data augmentation. The dataset is split into training, validation, and test sets for model evaluation. The performance of each model is assessed using confusion matrices, accuracy, sensitivity, and specificity. Results show varying accuracies for different liver disease classes, indicating the strengths and limitations of the models. To overcome the limits of the three-class classifiers, a framework for the Efficient Hybrid CNN method to classify Liver diseases (EHCNNLD) is proposed, combining the predictions from the three models with weighted probabilities. The Proposed EHCNNLD method demonstrates improved accuracy and classification power, enhancing the overall performance for liver disease classification. The study highlights the potential of deep learning techniques in medical image analysis and clinical diagnosis. The findings provide valuable insights into developing robust and accurate models for liver disease classification, paving the way for medical research and patient care advancements.