Enhanced image segmentation for cancer images using sparse based classification framework

The correct identification and class of cancerous regions in scientific pictures is vital for diagnosis and treatment planning. This examination recommends an enhanced image segmentation approach with a sparse-based total-type framework for most cancer pictures. Conventional segmentation techniques rely on hand-made functions and require guide tuning, leading to restrained accuracy and generalizability. Our proposed approach uses a sparse representation method, which mechanically learns discriminative functions from the statistics to enhance the accuracy of cancer place segmentation. Moreover, we include a classification framework, primarily based on a help vector device, to refine the segmentation consequences.