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Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach
Muhammad Nouman Noor1, Farah Haneef2, Imran Ashraf1
1School of Computing, National University of Computer & Emerging Sciences (FAST-NUCES), Islamabad 44000, Pakistan.
Bioengineering (Basel, Switzerland)
|March 28, 2025
Summary
This study introduces an advanced image processing and attention-based vision approach for enhanced skin disease diagnosis, improving classification accuracy for conditions like eczema and melanoma.
Area of Science:
- Dermatology and Medical Imaging
- Artificial Intelligence in Healthcare
Background:
- Skin diseases are common and require early diagnosis to prevent complications.
- Accurate classification of skin lesions is crucial for effective treatment.
Purpose of the Study:
- To enhance skin disease diagnosis using advanced image processing and an attention-based vision approach.
- To support dermatologists in classifying skin diseases accurately.
Main Methods:
- Image preprocessing techniques including adaptive histogram equalization, gamma correction, and contrast stretching.
- An attention-based classification model utilizing transformers and multi-head attention.
- Experimentation on two publicly available skin disease datasets.
Main Results:
- The proposed approach demonstrated robust performance on multiple datasets.
- Achieved competitive classification results compared to state-of-the-art methods.
- Validated the effectiveness of image enhancement and attention mechanisms.
Conclusions:
- The integrated approach of image processing and attention-based vision shows significant promise for improving dermatological diagnosis.
- This method can aid clinicians in solving complex skin disease classification tasks.
- Further research can explore broader applications in medical image analysis.