Malignant melanoma is a deadly form of skin cancer that requires early and accurate diagnosis for effective treatment. Machine learning (ML) techniques have shown promise in assisting dermatologists in diagnosing melanoma. Ensemble machine learning is a popular approach that combines multiple models to improve prediction accuracy. In this study, an ensemble machine learning technique is what we propose for classifying skin lesions into the melanoma or nonmelanoma class. We collected a dataset of skin lesion images, including melanoma and nonmelanoma cases, and used various feature extraction techniques to extract features from the images. We trained several ML models, including support vector machines (SVM), decision trees (DT), and random forests (RF), and combined them using ensemble techniques, such as bagging, boosting, and stacking. Our results show that the ensemble model outperforms individual models and achieves high accuracy in melanoma classification. The proposed approach can assist dermatologists in the early detection and diagnosis of melanoma, leading to timely treatment and improved patient outcomes.

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