Skin Cancer Detection Using Deep Learning Approaches

Aim: This review examined multiple deep learning (DL) methods, including artificial neural networks (ANNs), convolutional neural networks (CNNs), k-nearest neighbors (KNNs), as well as generative adversarial networks (GANs), relying on their abilities to differentially extract key features for the identification and classification of skin lesions. Background: Skin cancer is among the most prevalent cancer types in humans and is associated with tremendous socioeconomic and psychological burdens for patients and caregivers alike. Incidences of skin cancers have progressively increased during the last decades. Early diagnoses of skin cancers may aid in the implementation of more effective treatment and therapeutic regimens. Indeed, several recent studies have focused on early detection strategies for skin cancer. Among the lesion features that can aid the recognition and characterization of skin cancers are symmetry, color, size, and shape. Results: Our assessment indicates that CNNs delivered maximum accuracy in visual lesion recognition, yet GANs have surfaced as a strong tool for training augmentation through simulated image creation. However, there were significant limitations associated with existing datasets, such as provision of insufficient skin tone variability, demanding computational needs, and unequal lesion representations, which may hamper efficiency, inclusivity, and generalizability of DL models. Researchers must combine diverse high-resolution datasets within a structural framework to develop efficient computational models with unsupervised learning methods to enhance noninvasive and precise skin cancer detection. Conclusion: The breakthroughs in image-based computational skin cancer detection may be crucial in reducing the requirement of invasive diagnostic tests and expanding the scope of skin cancer screening toward broad demographics, thereby aiding early cancer detection in a time- and cost-efficient manner.