Detection of Skin Cancer Types in Dermoscopy Images with Gradient Boosting

This study develops a computer-based system for classifying using gradient booster algorithms that addresses the urgent demand for precise and prompt skin cancer diagnosis. Dermoscopy pictures were gathered and prepared for the extraction of features using an interpretivist strategy. A large dataset with a variety of skin lesions was used for testing and training purposes. The gradient amplifier model performed better than other models at classifying different types of skin cancer, with an F1-score of 0.92 as well as an accuracy of 94.5%. The effectiveness of the suggested technique was underlined by comparison with initial models. A graphical representation of the classification findings, including a confusion matrix and ROC curves, gave intuitive understandings of the model's discriminatory abilities. An examination of the feature importance indicated the crucial characteristics influencing accurate classification. Future research is advised to investigate different ensemble methods, incorporate multisensory data sources, and perform real-time therapeutic validations. This study highlights the possibilities potential models to be an important tool in skincare, enhancing patient care and providing early management in the identification of skin cancer