Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO)

One of the most dangerous types of cancer is skin cancer (SC), which is seen in the form of skin lesions in the patient and can threaten the patient's life if not treated on time. With early diagnosis of this disease, more effective treatment methods can be used and the progression of the disease can be prevented. Various machine learning and deep learning methods have been developed for early skin cancer diagnosis. However, one of the benefits of deep learning is the ability to learn enormous volumes of data which abundances the trend towards deep learning methods. In this article, a method based on the combination of EfficientNet and Wavelet is presented to classify skin cancer images. Various combinations of the EfficientNet approach, including B0, B1, B2 and B3, are considered for this purpose. Also, Gray Wolf Optimization (WGO) is used to find the optimal values of the features. Two datasets of ISIC-2016 and ISIC-2017 are considered for model evaluation. Based on the obtain results, the EfficientNetB3+Wavelet+GWO model obtained the best result on the ISIC-2016 data and is able to achieve an accuracy of 0.9814 and an F-measure of 0.9827. Furthermore, in the ISIC-2017 data set, the EfficientNetB1+Wavelet+GWO model achieves the best performance with an accuracy of 0.9795 and an F-measure of 0.9797 in ISIC-2017 data.