Prediction and Detection of Liver Diseases using Convolutional Neural Network

The liver is an essential organ in the human body, and early recognition and diagnosis of liver disease are crucial for effective treatment. Conventional methods, such as Liver Function tests, rely on test results that may not detect the disease in its early stages. Unfortunately, symptoms of liver disease often only become apparent in the later stages, making early detection challenging. To address this issue, we propose using Convolutional Neural Networks (CNNs) to facilitate early detection of liver disease and identify the elements that can lead to fatal liver impairment. CNNs are deep learning algorithms that have shown remarkable success in image recognition and classification tasks, making them a perfect fit for liver disease detection. Our initiative aims to use CNNs to distinguish between healthy individuals and those with liver disease. The CNN algorithm will classify the disease into its level and type if a person is found to have liver disease. Additionally, the system will provide precautionary measures based on any symptoms observed. The use of CNNs in liver disease detection represents a significant step forward in the field of medical diagnosis. By leveraging the power of deep learning algorithms, we can improve the accuracy and efficiency of liver disease diagnosis, potentially saving countless lives. With the help of CNNs, medical professionals can detect liver disease in its early stages when it is easier to treat, allowing for minimal medication and avoiding fatal liver impairment. Moreover, studies have shown that CNNs can be used for the detection of liver lesions, which are commonly associated with liver cancer. By analyzing medical images, CNNs can accurately detect liver lesions and help physicians make better-informed decisions about patient care. This further emphasizes the importance of using CNNs for liver disease detection and prediction