A novel diagnostic map for computer‐aided diagnosis of skin cancer

Abstract

In teledermoscopy, images are transmitted through a communication channel to the medical facility for medical consultation. This yields to bandwidth congestion and consumption of large storage size which impairs the transmission of the high-resolution dermoscopy image. This study proposes a novel technique by generating a diagnostic feature map, named diagonal compressive sensing (CS) features map. This proposed map is generated using the significant diagnostic features by aligning the feature vector in a diagonal map, which is then compressed using CS technique. Eventually, the recovered feature map at the receiver side is applied to the classification system for decision-making. In addition, physicians at the receiver side who were trained to read the feature maps can verify the classification decision, and then provide feedback to the patient. The results demonstrated that the proposed diagnostic map achieved less transmission time due to the small size of the feature map along with the compression process. Furthermore, the feature map has drastically improved the classification performance metrics, including the accuracy, which increased, for example, from 88.6% to 98% at 80% compression ratio compared to the traditional method on the compressed whole image.