ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES

Early detection of skin cancer, particularly melanoma, is crucial for improving patient outcomes and survival rates. Traditional diagnostic methods often require subjective interpretation by dermatologists, which can lead to inconsistent results. In recent years, machine learning algorithms, especially deep learning models such as Convolutional Neural Networks (CNNs), have shown promise in automating the analysis of medical images, enabling more accurate and efficient detection of skin cancer. This study investigates the performance of various machine learning models for skin cancer detection using the ISIC dataset, which consists of dermoscopic images. Six machine learning algorithms were evaluated: Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and CNN. The models were assessed based on their accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results demonstrated that CNN outperformed all other models in terms of accuracy, AUC, and F1-score, making it the most effective algorithm for skin cancer detection in this study. While traditional machine learning algorithms like Random Forest and SVM showed promising results, CNNs' ability to automatically extract relevant features from complex images provided a significant advantage. The findings suggest that CNNs are particularly well-suited for early-stage skin cancer detection, although challenges related to model interpretability and dataset variability remain. This study highlights the potential of machine learning in revolutionizing skin cancer diagnosis and paves the way for future research focused on improving model robustness and clinical integration.