High-Dimensional Block Feature Extraction and Deep Recurrent Multilayer Classification for Knee Injury Detection - Sowmiya, V.; Lavanya, V.S (Journal of Neonatal Surgery, 2025)
Knee injury exposure involves the specification for the estimation of abnormalities, damages irregularities within the knee joint and surrounding structures. In this process is critical for diagnosing various conditions, such as ligament injuries, cartilage damage and other soft tissue injuries. The research study suggests a novel approach for knee injury detection applying a crossbreed methodology combining radial kernel feature extraction and deep recurrent multilayer classification. Further it aims to boost the accuracy and efficiency of knee injury diagnosis through advanced computational techniques. Consequently, the initial step move to adaptive window median noise remove filter Pre-processing. Median-filtered values are used to recreate the signal, effectively removing noise while preserving signal features and adaptive window median filter dynamically adjusts the window size based on the local features of the signal and effectively remove the noise. Additional step, move to applying filtered Hybridization radial kernel segments are frequently overlapped and combined using approaches such as averaging or interpolation. Following step, move to High Dimensional Block feature extraction efficiently extracts important features from complex knee imaging data. Radial kernels are numerical functions used to measure the similarity between data points in a high-dimensional space. By applying radial kernel feature extraction, method identifies major patterns and structures within knee images and enabling better representation of acute information related to injuries. To conclude to Deep Recurrent Multilayer Classification Model allows for the accurate classification of knee injuries based on the extracted features, leveraging the hierarchical representation learned by way of multiple layers of processing and effectively enhanced knee injury detection and improve accuracy, smaller error rate and reduce knee injury detection time..