Multiview Radiographic Osteoarthritic Anomaly Detection Utilizing Enhanced Liquid Neural Network Architectures and Advanced Computational Imaging Techniques

In this digital era, Osteoarthritis (OA) could be a predominant degenerative joint clutter that altogether impacts the quality of life for millions around the world. Early location and exact conclusion are significant for compelling treatment, but routine radiographic investigation strategies frequently battle with exactness and consistency. In this paper, we propose a novel approach to Multiview Radiographic Osteoarthritic Irregularity Location by utilizing improved profound learning models and progressed computational imaging procedures. Our strategy consolidates Convolutional Neural Systems (CNN), Long Short- Term Memory (LSTM) systems, cross breed models combining CNN and LSTM, CNN with bidirectional LSTM (biLSTM), and LNN (Liquid Neural Network) with simple RNN layers. The models were trained on a comprehensive multiview radio- graphic dataset of osteoarthritic knee joints. Among the designs tried, LNN beated others, accomplishing the most noteworthy classification exactness of 91 percent, illustrating predominant include extraction and learning capacity in recognizing $O A$ related irregularities. The integration of progressed computational imaging and productive profound learning systems appears guarantee in progressing demonstrative exactness, clearing the way for mechanized and vigorous OA location frameworks in clinical settings. Our discoveries highlight the potential of LNN (Liquid Neural Network) as a state-of-the-art arrangement for radiographic peculiarity location, with critical suggestions for both demonstrative viability and persistent care