Design of Convolutional Neural Networks for Detection of Liver Disease

Abstract

Liver disease is a significant medical condition that affects millions of people worldwide. Early and accurate diagnosis is crucial for the treatment and management of liver disease. In recent years, Convolution Neural Networks (CNNs) have excelled in several medical image-processing applications, including disease diagnosis. The design and development of a convolution neural network for use in identifying liver disease from medical imaging data are the major subjects of this study. The proposed CNN architecture is designed to analyze medical images, especially liver images obtained using imaging methods like computed tomography (CT) scans or magnetic resonance imaging (MRI). The architecture uses input images to teach it hierarchical features through the use of several convolution layers. Additionally, pooling layers are employed for classification while fully linked layers are used to reduce computational complexity and spatial dimensions. To train and evaluate the CNN, a sizable liver imaging dataset comprised of both healthy and ill patients is used. To enhance image quality and ensure uniformity in terms of size and orientation, the dataset has been preprocessed. The network's parameters are tuned during the training process using the appropriate optimization techniques and loss functions. Testing and validation are carried out to assess the network's performance with unobserved data. The findings of the research demonstrate how effective the proposed CNN architecture is at detecting liver disease. As a result of the model's high accuracy rate, it may prove to be a valuable tool for assisting medical practitioners in spotting liver disorders. The study adds to the growing body of information on the application of deep learning algorithms in medical picture analysis and emphasizes the significance of early and precise disease identification.