Deep Learning for Skin Disease Classification: A Comparative Analysis of Convolutional and Transformer-Based Models

Sharmin Islam Shroddha; Rashedur Rahman; Ashraful Islam; Md Junayed Hossain; Sefatul Wasi; Saadia Binte Alam; M Ashraful Amin

doi:10.1109/scored68498.2025.11399043

Conference Paper2025

Deep Learning for Skin Disease Classification: A Comparative Analysis of Convolutional and Transformer-Based Models

Sharmin Islam Shroddha, Rashedur Rahman, Ashraful Islam, Md Junayed Hossain, Sefatul Wasi, Saadia Binte Alam, M Ashraful Amin

2025 IEEE 23rd Student Conference on Research and Development (SCOReD)

IEEE, pp. 1-6

DOI: 10.1109/scored68498.2025.11399043 URL

Abstract

Classifying skin diseases from dermoscopic images is important for the early detection and treatment of skin cancer, as timely diagnosis can significantly improve patient outcomes. In this study, we perform a comparative analysis of three widely used deep learning models EfficientNetB0, ResNet50, and Vision Transformer (ViT) for automated classification of dermoscopic images from the HAM10000 dataset. Since the dataset is highly imbalanced across the seven disease categories, we applied data augmentation and downsampling techniques to create a balanced set of 2,000 images per class. EfficientNetB0 and ResNet50 were trained using transfer learning and fine-tuning strategies, while the ViT was implemented and trained from scratch with customized hyperparameters. The models were evaluated using accuracy, precision, recall, F1-score, and AUC to provide a comprehensive assessment of their performance. Experimental results demonstrate that EfficientNetB0 achieved the highest performance with 91% accuracy and an average AUC of 0.99, closely followed by ViT with 90% accuracy and an AUC of 0.99, while ResNet50 reached 86% accuracy with an AUC of 0.98. These findings suggest that lightweight architectures such as EfficientNetB0 can outperform deeper convolutional networks and achieve results comparable to transformer-based approaches.

Keywords

ResNet50
skin disease classification
vision transformer
dermoscopic images
EfficientNetB0
HAM10000 dataset
transfer learning
skin cancer detection
class imbalance
data augmentation

CCDS Authors

Md. Rashedur Rahman, D. Eng.

Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD

Center Director, CCDS
Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Saadia Binte Alam, PhD

Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Prof. M Ashraful Amin, PhD

Founder Director, CCDS
Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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