Hybrid AI Models for Dermatology: Combining CNN and RF for Skin Lesion Classification

Accurate and timely diagnosis of skin lesions is crucial for effective treatment. This research proposes a novel hybrid model that combines Convolutional Neural Networks (CNNs) and Random Forests (RFs) to improve the classification of skin lesions. The CNN component extracts relevant features from dermoscopic images, while the RF classifier categorizes these features into different skin lesion types. The hybrid model benefits from the strengths of both techniques, integrating the feature extraction capabilities of CNNs and the robust classification performance of RFs. The proposed model achieved a high accuracy of 94%, surpassing the performance of individual CNN and RF models. The results demonstrate the effectiveness of the hybrid approach in accurately classifying skin lesions, contributing to early detection and appropriate treatment. This research has significant implications for dermatology, offering a potential solution to improve diagnostic accuracy and patient outcomes.