XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging
Age-related macular degeneration (AMD) is a leading cause of visual impairment in adults over 50 years of age and is characterized by progressive damage to the macula, leading to challenges in daily activities such as reading and driving. This study introduces the XV-AMD (An Explainable Vision Trans...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11052213/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849320329334226944 |
|---|---|
| author | Aya A. Abd El-Khalek Hossam Magdy Balaha Mohamed T. Azam Ashraf Sewelam Mohammed Ghazal Abeer T. Khalil Mohy Eldin A. Abo-Elsoud Ayman El-Baz |
| author_facet | Aya A. Abd El-Khalek Hossam Magdy Balaha Mohamed T. Azam Ashraf Sewelam Mohammed Ghazal Abeer T. Khalil Mohy Eldin A. Abo-Elsoud Ayman El-Baz |
| author_sort | Aya A. Abd El-Khalek |
| collection | DOAJ |
| description | Age-related macular degeneration (AMD) is a leading cause of visual impairment in adults over 50 years of age and is characterized by progressive damage to the macula, leading to challenges in daily activities such as reading and driving. This study introduces the XV-AMD (An Explainable Vision Transformer Detection for Age-Related Macular Degeneration) framework, which uses Vision Transformers (ViTs) and SHapley Additive exPlanations (SHAPs) for the diagnosis and explainability of AMD. Through a systematic preprocessing pipeline, including average cumulative distribution function (CDF) calculation and contrast limited adaptive histogram equalization (CLAHE), our approach enhances image quality and prepares data for analysis. We classify color fundus images into four distinct AMD categories, namely, normal, geographic atrophy (GA), intermediate AMD, and wet AMD, demonstrating the framework’s effectiveness in a clinical context. The experimental results reveal that our ViT-based model not only improves diagnostic accuracy but also facilitates the understanding of decision-making processes, thus supporting early detection and intervention strategies in AMD management. In addition, a 99.54% classification accuracy was attained, with a sensitivity of 99.54%, specificity of 99.85%, precision of 99.54%, and F1 of 99.54%. This research shows the promise of AI and deep learning in improving AMD diagnoses and identifies future paths for boosting model performance and interpretability in medical imaging applications. |
| format | Article |
| id | doaj-art-457a0239b3f54d2eb46c3b5cfdf10565 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-457a0239b3f54d2eb46c3b5cfdf105652025-08-20T03:50:07ZengIEEEIEEE Access2169-35362025-01-011311396711398310.1109/ACCESS.2025.358355511052213XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus ImagingAya A. Abd El-Khalek0Hossam Magdy Balaha1https://orcid.org/0000-0002-0686-4411Mohamed T. Azam2Ashraf Sewelam3Mohammed Ghazal4https://orcid.org/0000-0002-9045-6698Abeer T. Khalil5Mohy Eldin A. Abo-Elsoud6Ayman El-Baz7https://orcid.org/0000-0001-7264-1323Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, EgyptBioengineering Department, J. B. Speed School of Engineering, University of Louisville, Louisville, KY, USABioengineering Department, J. B. Speed School of Engineering, University of Louisville, Louisville, KY, USAOphthalmology Department, Faculty of Medicine, Mansoura University, Mansoura, EgyptElectrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab EmiratesCommunications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, EgyptCommunications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, EgyptBioengineering Department, J. B. Speed School of Engineering, University of Louisville, Louisville, KY, USAAge-related macular degeneration (AMD) is a leading cause of visual impairment in adults over 50 years of age and is characterized by progressive damage to the macula, leading to challenges in daily activities such as reading and driving. This study introduces the XV-AMD (An Explainable Vision Transformer Detection for Age-Related Macular Degeneration) framework, which uses Vision Transformers (ViTs) and SHapley Additive exPlanations (SHAPs) for the diagnosis and explainability of AMD. Through a systematic preprocessing pipeline, including average cumulative distribution function (CDF) calculation and contrast limited adaptive histogram equalization (CLAHE), our approach enhances image quality and prepares data for analysis. We classify color fundus images into four distinct AMD categories, namely, normal, geographic atrophy (GA), intermediate AMD, and wet AMD, demonstrating the framework’s effectiveness in a clinical context. The experimental results reveal that our ViT-based model not only improves diagnostic accuracy but also facilitates the understanding of decision-making processes, thus supporting early detection and intervention strategies in AMD management. In addition, a 99.54% classification accuracy was attained, with a sensitivity of 99.54%, specificity of 99.85%, precision of 99.54%, and F1 of 99.54%. This research shows the promise of AI and deep learning in improving AMD diagnoses and identifies future paths for boosting model performance and interpretability in medical imaging applications.https://ieeexplore.ieee.org/document/11052213/Deep learning (DL)computer aided diagnosis (CAD)eXplainable artificial intelligence (XAI)age-related macular degeneration (AMD) |
| spellingShingle | Aya A. Abd El-Khalek Hossam Magdy Balaha Mohamed T. Azam Ashraf Sewelam Mohammed Ghazal Abeer T. Khalil Mohy Eldin A. Abo-Elsoud Ayman El-Baz XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging IEEE Access Deep learning (DL) computer aided diagnosis (CAD) eXplainable artificial intelligence (XAI) age-related macular degeneration (AMD) |
| title | XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging |
| title_full | XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging |
| title_fullStr | XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging |
| title_full_unstemmed | XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging |
| title_short | XV-AMD: An Explainable Vision Transformer Detection Framework for Age-Related Macular Degeneration Using Fundus Imaging |
| title_sort | xv amd an explainable vision transformer detection framework for age related macular degeneration using fundus imaging |
| topic | Deep learning (DL) computer aided diagnosis (CAD) eXplainable artificial intelligence (XAI) age-related macular degeneration (AMD) |
| url | https://ieeexplore.ieee.org/document/11052213/ |
| work_keys_str_mv | AT ayaaabdelkhalek xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT hossammagdybalaha xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT mohamedtazam xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT ashrafsewelam xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT mohammedghazal xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT abeertkhalil xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT mohyeldinaaboelsoud xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging AT aymanelbaz xvamdanexplainablevisiontransformerdetectionframeworkforagerelatedmaculardegenerationusingfundusimaging |