Abstract: Liver diseases are becoming more prevalent and can cause significant morbidity and mortality. The identification of liver maladies is a pivotal medical procedure that can have a momentous impact on the final results of patients. The traditional method of diagnosis involves the use of medical imaging and biopsy, but these techniques can be intrusive, time-consuming, and expensive. The recent development of machine learning has opened up the possibility of a non-invasive and more efficient approach to the diagnosis of liver diseases. The use of convolutional neural network (CNN) models for the identification of liver maladies from computed tomography (CT) scan images is a promising method that has received a significant amount of attention from the medical community. These models can precisely detect and categorize different types of liver maladies, such as fatty liver disease and hepatitis, with an exceptional degree of accuracy. The CNN model is instructed using an extensive dataset of CT scan images of the liver, and the network is created to recognize the patterns and features that are peculiar to various liver diseases. Once trained, the model can categorize new CT scans into different groups based on their visual features, providing clinicians with a potent tool for precise and efficient diagnosis. The use of machine learning-based approaches for liver disease diagnosis has numerous advantages, including reduced invasiveness, improved accuracy, and lower costs. It has the potential to transform the way liver diseases are diagnosed and managed, resulting in improved patient outcomes and enhanced quality of care. Nevertheless, further research is necessary to substantiate these approaches and ensure their safety and effectiveness in clinical practice.
Keywords: Liver Diseases, Machine Learning, Convolutional Neural Network, Diagnosis, CT scan
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