Abstract
Skin cancer is one of the most widespread threats to human health worldwide. Therefore, early-stage recognition and detection of these diseases are crucial for patients' lives. Computer-aided methods can be used to solve this problem with high performance. We presented a wavelet transform-based deep residual neural network (WT-DRNNet) for skin lesion classification. In the proposed model, wavelet transformation, pooling, and normali-zation reveal more refined details and eliminate unwanted details from skin lesion images. Then, deep features are extracted with the residual neural network based on transfer learning as a feature extractor. Finally, these deep features were combined with the global average pooling approach, and the training phase was carried out using the Extreme Learning Machine based on the ReLu activation function. The ISIC2017 and HAM10000 datasets were used in the experimental works to test the proposed model's performance. The performance metrics of accuracy, specificity, precision, and F1-Score of the proposed model for the ISIC2017 dataset were 96.91%, 97.68%, 96.43%, and 95.79%, respectively, while these metrics for the HAM10000 dataset were 95.73%, 98.8%, 95.84%, and 93.44%, respectively. These results outperform the state-of-the-art to classify skin lesions. As a result, the proposed model can assist specialist physicians in automatically classifying cancer-based on skin lesion images.