Deep Learning Technique to Extract Skin Cancer in Earlier Stage Through Optimization

Early detection of skin cancer represents an urgent medical need worldwide because it remains among the most common dangerous diseases that kills patients. The research introduces an optimized deep learning technique which uses convolutional neural network (CNN) technology to boost skin cancer detection results. The proposed model delivers 96.2% accuracy which surpasses machine learning procedures of prior generations. Testing revealed both AUC-ROC scores and confusion matrix results which establish its capacity to identify benign and malignant conditions effectively. Training stability improves alongside overfitting reduction thanks to the addition of optimization techniques One-Cycle Learning Rate Policy together with Bayesian Optimization and AdamW optimizer in the model. The model's response time has been decreased to 10 hours, which makes it compatible for running clinical operations in real time. The model demonstrates potential for supporting dermatologists' diagnostic abilities which will help achieve better patient outcomes and decrease mortality statistics. Further research should focus on enabling real-time mobile applications to work with the model for expanded diagnostic accessibility.