Early Stage Skin Cancer Detection Using Deep Learning: A Comprehensive Model for Improved Treatment Outcomes and Survival Rates

Abstract

Detecting skin cancer early on greatly increases survival chances and treatment results.  This work offers a deep learning-based algorithm for spotting skin cancer in its earliest stages.  We created a classification model based on 10,015 dermatoscopic images spanning seven skin cancer types using transfer learning and a convolutional neural network architecture.  On the test dataset, our model had 91.3% accuracy, 89.7% sensitivity, and 94.2% specificity.  With an AUC of 0.956, the model outperforms earlier methods in identifying early-stage melanoma.  The model's interpretability was improved by means of attention mechanisms and feature visualisation, hence offering visual justifications for forecasts that can help dermatologists make therapeutic decisions.  By offering a strong framework for early skin cancer identification that strikes a compromise between great diagnostic accuracy and clinical interpretability, our work adds to the expanding area of AI-assisted dermatology.