Application of Machine Learning and Deep Learning in Medical Image Segmentation - an Example of Liver CT Images

In the realm of modern medicine, the process of liver image segmentation holds immense significance in facilitating precise diagnoses of hepatic diseases. However, this procedure is currently confronted with numerous challenges, including the challenges of achieving optimal accuracy and reliability in the results obtained. This study focuses on the application of machine learning and deep learning techniques to computed tomography (CT) liver images. The study utilizes support vector machine and convolutional neural network architectural models to assess the performance of the algorithms from diverse perspectives through experimental evaluations. The results of this investigation show the efficacy of a deep learning model in large-scale image segmentation, exhibiting superior accuracy and processing speed. In contrast, the machine learning algorithm demonstrates superiority in singleimage processing. The innovation of the machine learning algorithm is achieved by incorporating an attention module and implementing multi-scale feature fusion, thereby boosting the model's practical performance and providing more reliable support for medical diagnosis.