Comprehensive Analysis of Deep Learning for Accurate Knee Joint Segmentation and Classification

Knee joint segmentation and classification are critical tasks in medical imaging, having applications in diagnosis, treatment planning, and surgical navigation. The intricate architecture of the knee joint and the variation in knee joint imaging, however, can make these tasks difficult. Deep learning has become a viable method for classifying and segmenting knee joints. From massive knee joint picture datasets, deep learning models may be trained to recognize intricate patterns, and they can produce state-of-the-art performance on certain tasks. In order to divide and classify knee joints, this paper discusses current developments in deep learning. The paper talks about the various training approaches and deep learning architectures applied to these challenges. Furthermore, the paper provides an extensive summary of the cutting-edge functionality of deep learning.