Boosting Skin Lesion Classification with a Class Expert DCGAN Framework for Skin Disease Detection

Skin lesion classification using deep learning techniques is challenged by insufficient samples and class imbalances in datasets. This study introduces a novel framework, the class expert Deep Convolutional Generative Adversarial Network (DCGAN), designed to handle class imbalance and enhance classification accuracy for under represented classes. The proposed framework also leverages weight transfer from the GAN discriminator trained on each class to expert layers, which are then modified to classify skin lesion images more accurately using the discriminator's weights. This transfer learning strategy enhances the performance of the Convolutional Neural Network (CNN) model in DCGAN by utilizing the discriminative features learned during GAN training. Experimental evaluations demonstrate that the proposed class expert DCGAN framework achieves notable improvements in accuracy and precision, particularly for classes with fewer samples. Specifically, it achieves a 2-3% increase in classification accuracy compared to traditional methods. These results underscore the effectiveness of leveraging GANs for data augmentation and discriminative feature extraction in medical image classification. Thus, the class expert DCGAN framework offers a promising solution to improve the performance of skin lesion classification models, facilitating highly reliable diagnostic decisions and enhancing the interpretation of dermatological images across diverse clinical scenarios.