<![CDATA[Skin cancer, particularly melanoma, poses significant challenges to public health, with early detection being critical for effective treatment. Traditional diagnostic methods often fall short, particularly in resource-limited settings. In response, artificial intelligence (AI) techniques, especially deep learning models, have emerged as promising tools for automated skin cancer detection. This study evaluates the performance of Dense Convolutional Neural Networks (DCNNs) in classifying and detecting skin lesions, leveraging simulation-based approaches to assess the effectiveness of various AI models. Utilizing datasets such as HAM10000 and ISIC2017, which contain a wide variety of skin types and lesion stages, the models were trained and tested using key performance metrics such as accuracy, precision, recall, and F1-score. The results shows that DCNNs outperformed traditional machine learning techniques like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees (DT), demonstrating superior accuracy, generalization ability, and efficiency in handling large, imbalanced datasets. The simulation-based approach provided insights into the ability of DCNN models to manage dataset inconsistencies and class imbalances, showcasing their potential as robust tools for skin cancer detection. These findings highlight the ability of AI in advancing dermatological diagnostics, offering more timely and accurate detection, and potentially improving patient outcomes]]>
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