Skin Cancer Classification Using Deep Spiking Neural Network

Skin cancer is one of the primary causes of death globally, and experts diagnose it by visual inspection, which can be inaccurate. The need for developing a computer-aided method to aid dermatologists in diagnosing skin cancer is highlighted by the fact that early identification can lower the number of deaths caused by skin malignancies. Among computer-aided techniques, deep learning is the most popular for identifying cancer from skin lesion images. Due to their power-efficient behavior, spiking neural networks are attractive deep neural networks for hardware implementation. We employed deep spiking neural networks using the surrogate gradient descent method to classify 3670 melanoma and 3323 non-melanoma images from the ISIC 2019 dataset. We achieved an accuracy of 89.57% and an F1 score of 90.07% using the proposed spiking VGG-13 model, which is higher than the VGG-13 and AlexNet using less trainable parameters.