Skin Lesion Classification Using Deep Learning

Skin lesion is one of the most common types of cancer globally, and early recognition is essential for a better prognosis for patients. In this paper, we built a machine learning-based image classification system to classify dermoscopic skin lesion images into a range of diseases. We adopted the publicly available HAM10000 dataset and preprocessed it for model optimization and the prevention of overfitting, including image resizing, normalization, and data augmentation. We also investigated class distribution to see the data imbalance and give a hint for model training. To demonstrate the classification ability, we conducted the experiment with four models, including SVM, CNN, VGG16, and ResNet50.The SVM achieved an accuracy of approximately 80%, while the CNN improved this to 92%. The VGG16 model further increased the accuracy to 94%. ResNet50 outperformed all other models, achieving the highest accuracy of 95%. Our results demonstrate that deep learning models, particularly ResNet50 and VGG16, are highly effective in skin lesion classification and have significant potential for supporting early skin cancer diagnosis and aiding healthcare professionals in clinical decision-making