SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset

Skin cancer is a fatal disease that has become the leading cause of death worldwide in recent years, although it is curable if diagnosed early. Early skin cancer detection significantly improves patients' chances of survival and reduces mortality. In this research, we conduct experiments on a high imbalance dermoscopic ISIC 2020 dataset. The primary objective of this study is to develop a shallow CNN architecture to complete the classification task effectively, requiring fewer computational resources without compromising accuracy. We have used pre-processing techniques to remove image noise and truncation and augmentation techniques to balance the dataset before fitting it into the model. Multiple performance measurement metrics were utilized to establish the overall performance. Our proposed model yields a remarkable test accuracy of 98.81%. We compare our models' performance with different transfer learning (TL) models to assess the faster convergence rate. The proposed model demonstrated its robustness by outperforming the other TL models in terms of accuracy within a short processing time. It is reasonable to assume that our proposed system will reliably aid dermatologists in diagnosing skin cancer patients early and increasing survival rates.