Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques

Skin cancer, a severe health threat, can spread rapidly if undetected. Therefore, early detection can lead to an advanced and efficient diagnosis, thus reducing mortality. Unsupervised classification techniques analyse extensive skin image datasets, identifying patterns and anomalies without prior labelling, facilitating early detection and effective diagnosis and potentially saving lives. In this study, the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic images. The authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature extraction. To achieve this, enhanced super-resolution generative adversarial networks (ESRGAN) was fine-tuned to strengthen the resolution of skin lesion images, making critical features more visible. The authors extracted histogram features to capture essential colour characteristics and used the Davies–Bouldin index and silhouette score to determine optimal clusters. Fine-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets, respectively. The unsupervised approach effectively categorises skin lesions, indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.