Published Paper Details:

Skin lesion diagnosis plays a crucial role in early detection and treatment of skin diseases, including potentially malignant melanomas. This research presents an automated approach to skin lesion diagnosis using a combination of deep learning feature extraction and machine learning classifiers. The proposed method involves preprocessing skin lesion images, extracting discriminative features using the VGG16 deep learning model pre-trained on ImageNet, and employing various machine learning classifiers such as Support Vector Machine, Random Forest, Extra Trees, and Multi-layer Perceptron for classification. A comprehensive comparative analysis of these classifiers is conducted to evaluate their performance in terms of accuracy. Results demonstrate that the Support Vector Machine classifier achieves the highest accuracy of 89.16%, outperforming other classifiers. The proposed approach offers a promising solution for accurate and efficient skin lesion diagnosis, potentially aiding dermatologists in clinical decision-making and improving patient outcomes.