Enhancing Liver Cancer Diagnosis Through Advanced Image Processing Techniques: A CNN and Logistic Regression Approach

In our body, billions of cells are divided each day to form a new cell; it's just the replacement of the dead cells. Cellstogether form tissues, which in turn form organs. In a few cases, the cells are divided more than needed., forming lumps or growths called tumop;6/urs. The major death factor which is seen across the world is Liver cancer. Generally, this kind is not found at the beginning stages, and it becomes difficult to diagnose and treat. For any clinical support, early diagnosis with proper information using advanced segmentation methods can save time compared to traditional methods, which will help us in decision support. In recent years, medical image processing has become an essential part of diagnosing and accumulating diseases in formation to add clinical decision support. This paper uses convolutional neural networks (CNN) and logistic regression to observe and analyze treatment results for infected liver. As per the analysis, the convolution neural network performs better than logistic regression, with an accuracy of 96.22%.