Skin disease identification using transformer and cnn-based pre-trained models

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dc.contributor.authorSYED WAJID ALI
dc.date.accessioned2025-12-20T11:32:48Z
dc.date.available2025-12-20T11:32:48Z
dc.date.issued2025
dc.description.abstractSkin diseases affect millions of people worldwide and pose a significant diagnostic challenge due to their visual similarity and diversity. This study proposes a deep learning-based approach to classifying 23 types of skin diseases by developing and training models that utilize dermoscopic image data, to enhance diagnostic accuracy. Many deep learning models were tested under the same conditions. VGG16, MobileNetV2, EfficientNet-B0, and ViT Transformer. All of the models, EfficientNetB0 has the highest total accuracy, reaching 0.9377. and for others are MobileNetV2(0.92), DenseNet121 (0.664), ResNet50 (0.255), VGG16 (0.7944) and ViT (0.61). Nevertheless, in the proposed EfficientNet-B0 model, we are using a pre-created dataset by Dermnet from Kaggle, which contains 23 classes of skin diseases. The proposed EfficientNetB0, with an accuracy of 93.77%, precision of 96%, and a recall of 93% had an F1-score of 94%, surpassing all other CNN-based and Transformer-based models of architectures that were tested. Preprocessing procedures such as class normalization, data expansion, and imbalance have significantly improved the output and generalization of the model. Experiments on different architectures and adjustments of hyperparameters proved essential to optimizing results. Evaluation using a confusion matrix and ROC curves confirmed the ability of the model to effectively distinguish visually similar skin diseases. This test shows that improves classification performance, supports dermatologists in clinical decision-making, reduces diagnostic subjectivity, and ultimately provides scalable and reliable equipment to contribute to more accurate and timely treatment of skin diseases.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/17360
dc.language.isoen
dc.publisherUMT Lahore
dc.titleSkin disease identification using transformer and cnn-based pre-trained models
dc.typeThesis
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