Designing a Two-Stream Network Based Unsupervised Learning for Skin Cancer Recognition

Computer vision is crucial in identifying and diagnosing diseases like skin cancer, which can become life-threatening if not detected early. Although numerous methods have been developed, these techniques often face challenges due to the varied nature of skin cancer, which frequently presents irregular shapes and ambiguous structures. In this study, we introduce the design of an unsupervised two-stream network capable of simultaneously learning from datasets of various sizes. The network parameters are organized from smallest to largest to improve the efficiency of feature extraction. Additionally, the network incorporates residual blocks, bidi- rectional long short-term memory, and an attention layer to help reduce training loss. The proposed method was tested using the PAD-UFES-20 dataset, using mean square error to measure training loss and accuracy to check how well it recognizes skin cancer. The results showed a loss of 0.0079 and a training time of only 0.53 minutes, performing better than other advanced methods in both loss and speed. Our approach showed better results than previous methods, accurately recognizing skin cancer and showing potential for use in a mobile app to help with early detection and diagnosis.