Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks

•In this study, skin tumor images were classified as benign and malignant.•The dataset has been rebuilt by the Autoencoder model.•In the study, the MobileNetV2 convolutional model was used for training.•The features extracted from the datasets by the MobileNetV2 model have been combined.•Spiking networks contributed to the MobileNetV2 model by training the feature set. Melanocytes are skin cells that give color to the skin and form melanin color pigments. The unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis and proper treatment of skin cancer are so important. In this scope, a novel model that relies upon the autoencoder, spiking, and convolutional neural networks is proposed to ensure a useful decision support tool in this study. The experiments were carried out on an open-access dataset called the ISIC skin cancer consisting of 1800 being and 1497 malignant tumor images. In the proposed approach, the dataset is reconstructed using the autoencoder model. The original dataset and structured dataset were trained and classified by the MobileNetV2 model that consists of residual blocks, and the spiking networks. The classification success rate of the study was 95.27%. As a result, it was seen that the autoencoder model and spiking networks contributed to enhancing the performance of the MobileNetV2 model. Thanks to the proposed model, a novel fully automated decision support tool with high sensitivity was ensured for skin cancer detection. [Display omitted]