Optimization driven model and segmentation network for skin cancer detection

Abstract

The cancers in skins are considered as most risky disease. Recently, occurrence of skin cancer is considerably noticed among people globally. Earlier detection of skin cancer can result in reduced death rate. Dermoscopy is an effective way to detect and classify skin cancer. Since the visual assessment of dermoscopic images is a dull and cumbersome process, automated tools using computer aided diagnosis (CAD) model becomes essential. The current advances in machine learning (ML) such as deep learning (DL) has been considerably developed in the healthcare sector. The contemporary technical designs and techniques make it liable to discover this type of cancer effectually, but automated localization and skin lesion segmentation at prior phases is a complex task because of less contrast. This paper present an optimization based model to discover skin cancer using set of images. At primary, the input image is generated from a database and it is pre-processed using Gaussian filter and Region of Interest (ROI) extraction, which removes noises and mines interesting regions. The segmentation is performed with proposed U-RP-Net. Here, the proposed U-RP-Net model is obtained by integrating U-Net and RP-Net. Meanwhile, the output obtained from both U-Net and RP-Net model is integrated using Jaccard similarity-based fusion model. The data augmentation is processed for improving the detection performance. Finally, skin cancer finding is done using SqueezeNet. Moreover, the SqueezeNet is trained by proposed Aquila Whale Optimization (AWO) algorithm. The developed AWO approach is newly devised by integrating Aquila Optimizer (AO) and Whale Optimization Algorithm (WOA). The developed AWO-based SqueezeNet outperformed by highest testing accuracy of 92.5%, sensitivity of 92.1% and specificity of 91.7%.