<![CDATA[Skin cancer is one of the most threatening diseases to human health, with an increase in new cases each year. Early detection plays a crucial role in improving recovery rates, however, conventional diagnostic methods such as biopsy are often invasive, time-consuming, and costly. To address this issue, artificial intelligence-based diagnostic systems, particularly Convolutional Neural Networks (CNNs), offer a promising solution for enhancing diagnostic accuracy and efficiency. This study aims to evaluate the performance of a CNN model that combines Max Pooling and Global Average Pooling (GAP) in detecting skin cancer from digital dermoscopic images. The ISIC (International Skin Imaging Collaboration) dataset was used, focusing on two classes: malignant and benign. The combination of Max Pooling and GAP is intended to increase model precision while reducing the risk of overfitting. The experimental results show that the proposed model achieved a precision of 96.35%, indicating strong performance in minimizing false positives. However, the recall was relatively low at 85.99%, suggesting reduced sensitivity in detecting malignant cases. The overall accuracy of the combined model was 91.68%, slightly lower than the Max Pooling-only model (91.79%). Although the combination does not significantly improve accuracy, it effectively enhances precision to 96.35%. This is a critical advantage in a clinical setting, as it directly translates to minimizing false positive diagnoses and preventing patients from undergoing unnecessary invasive procedures like biopsies.]]>
Copyrights © 2025
