SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

Abstract

The increment in radiation in atmosphere causing wide spread of skin cancer disease around the globe. Cancer of the skin is one of the most lethal forms of the illness, and it is responsible for millions of deaths annually all over the world. Because of this, the early identification of skin cancer using computer-aided diagnostic (CAD) technology might potentially save a person's life.  However, skin cancer classification of performance is affected by hair artifacts and improper segmentation of lesion. The conventional methods were failed to achieve higher performance due to improper feature analysis. Therefore, to achieve the robust performance, this work was developed the transfer learning-based skin cancer detection and classification (SCDC-Net). Initially, UG-Net model was introduced to eliminate hair from skin lesions by enhancing the region of interest, which is developed by combining U-shaped deep learning network (U-Net) with generative adversarial networks (GAN). In addition, Hybrid U-Net (HU-Net) perform the segmentation operation for identifying the disease effected region of interest. Further, the features are extracted using gray level cooccurrence matrix (GLCM) matrix and discrete wavelet transform (DWT), which extracts the color, texture, statistical features. Finally, these combined characteristics are used to train a deep q neural network (DQNN) model, which then classifies a number of different skin cancer illness categories. The simulations conducted on real-time ISIC-2019 dataset proved that proposed SCDC-Net outperformed in all performance measures as compared to existing methods.