Implementing Deep Learning to Identify Skin Cancer from Dermoscopy Images

Traditionally, the identification of skin cancer from dermoscopy images has depended on invasive biopsies and subjective visual examination, which has caused delays and variability at the beginning of therapy. The study proposes a DL-based approach to automate diagnostics in order to overcome these problems. By utilizing DenseNet architecture, a convolutional neural network (CNN) pretrained on ImageNet, the proposed system improves diagnostic accuracy by acquiring complex lesion information. By minimizing bias and enhancing generalization, dataset preprocessing ensures variety and standardization. Transfer learning optimizes the model for dermatological patterns by fine-tuning it. Comprehensive assessment of training, validation, and testing sets yields better performance metrics than current approaches, with 92.5% accuracy, 89.0% sensitivity, 94.0% specificity, and 95.0% F1-score. Reductions in training time (12 hours) and inference time (18 ms/image) over existing systems further improve computational efficiency. With its potential to improve clinical results and efficiency in dermatological practice, the proposed method represents a significant leap in the diagnosis of skin cancer.