Convolutional Neural Network-based Multi-Classification of Skin Disease with Fine-Tuned ResNet50 and VGG16

Skin disorders present a significant global public health concern, affecting millions of individuals and contributing to high morbidity rates. The application of artificial intelligence, particularly deep learning, has emerged as a promising avenue for the automated classification and detection of skin diseases, offering potential improvements in accuracy, speed, and cost-effectiveness in dermatological diagnostics.

This study aims to evaluate the performance of convolutional neural networks (CNNs) in classifying skin diseases. Four widely recognized architectures—ResNet50, InceptionV3, EfficientNetB0, and VGG16—were implemented and compared using a dataset comprising 1,159 dermoscopy images across eight disease categories. Models were trained using the Adam optimizer with a batch size of 32 over 20 epochs. Performance metrics were assessed and benchmarked against findings from existing literature.

Among the evaluated models, EfficientNetB0 achieved the highest precision at 96.76%, followed by InceptionV3 and ResNet50 with accuracies around 93.5%. VGG16 demonstrated the lowest performance, achieving an accuracy of 84.32%. These results indicate that EfficientNetB0 offers superior feature extraction and generalization capabilities for dermatological image classification.

The findings suggest that recent CNN architectures, particularly EfficientNetB0, can significantly enhance the accuracy of skin disease classification. These improvements may facilitate more effective and scalable diagnostic tools in dermatology. Limitations of this study include the relatively small dataset and limited class diversity, which may affect model generalizability.

EfficientNetB0 outperformed ResNet50, InceptionV3, and VGG16 in classifying skin diseases, highlighting its potential for clinical application. Future research should focus on expanding datasets, refining model architectures, and deploying automated skin disease screening systems in real-world healthcare settings.