Multi-Model Deep Neural Network based Features Extraction and Optimal Selection Approach for Skin Lesion Classification

Melanoma skin cancer is one of the most deadly forms of cancer which are responsible for thousands of deaths. The manual process of melanoma diagnosis is a time taking and difficult task, therefore researchers introduced several computerized methods for recognition. Through computational methods, improves the accuracy of diagnostics process which is helpful for dermatologists. In this paper, we proposed an automated system for skin lesion classification through transfer learning based deep neural network (DCNN) features extraction and kurtosis controlled principle component (KcPCA) based optimal features selection. The pre-trained ResNet deep neural network such as RESNET-50 and RESNET-101 are utilized for features extraction. Then fused their information and selects the best features which later fed to supervised learning method such as SVM of radial basis function (RBF) for classification. Three datasets name HAM10000, ISBI 2017, and ISBI 2016 are utilized for experimental results and achieved an accuracy of 89.8%, 95.60%, and 90.20%, respectively. The overall results show that the performance of the proposed system is reliable as compared to existing techniques.