<![CDATA[Skin cancer remains one of the most common and serious global health problems, with cases continuing to increase annually. Early and accurate detection is essential for improving patient survival; however, conventional diagnostic methods often depend on manual visual assessment, which can be subjective and inconsistent. Hence, the development of an automated and reliable detection system is vital to support healthcare professionals in early diagnosis. This study proposes an intelligent diagnostic model for early skin cancer detection using dermatoscopic images, integrating transfer learning with Convolutional Neural Network (CNN) techniques. The model employs the HAM10000 dataset from the International Skin Imaging Collaboration (ISIC), which contains high-resolution dermatoscopic images classified into three malignant skin cancer types: Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), and Malignant Melanoma (MM). The CNN framework was built using pre-trained models optimized to enhance classification accuracy. Experimental results showed that the model achieved an accuracy of 96.67% and an F1-score of 0.97, demonstrating strong capability in identifying multiple malignant lesions. These findings indicate that the model can assist dermatologists and clinicians in improving diagnostic precision and reducing examination time in clinical practice. In conclusion, integrating transfer learning within a CNN architecture significantly improves classification efficiency even with limited data, and with further validation, the model shows strong potential for real-world implementation as an accurate, efficient, and accessible computer-aided diagnostic tool for early skin cancer detection.]]>
