Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique

This study introduces an innovative unsupervised method for medical image feature extraction, leveraging spatial stratification techniques. To expedite image recognition, the study proposes a novel weight-based objective function. The algorithm segments image pixels into multiple subdomains and accesses the image through a quadtree structure. Additionally, a threshold optimization technique utilizing the simplex algorithm is presented. To address the nonlinear characteristics of hyperspectral images, a kernel function-based generalized discriminant analysis algorithm is proposed. The project focuses on hyperspectral remote sensing images, exploring their mathematical modeling, solution methods, and feature extraction techniques. The findings indicate that different object types remain independent and exhibit compactness during image processing. Compared to traditional linear discrimination methods, this approach yields superior image segmentation results. This method not only mitigates the traditional method's susceptibility to lighting variations but also enables rapid and accurate feature extraction, offering significant reference value for clinical diagnosis.