Comparing AI Decision-Making with Expert Biomarkers: A Case Study on Diabetic Retinopathy Classification
| dc.contributor.author | Sharma, Aayushma | |
| dc.contributor.author | Paing, May Phu | |
| dc.date.accessioned | 2026-08-06T10:49:21Z | |
| dc.date.available | 2026-08-06T10:49:21Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.abstract | Artificial intelligence (AI) has become prevalent in the healthcare sector due to its ability to interpret complex medical images that may not be apparent to humans. Traditional black-box models were used to classify the disease, providing no information as to why certain things were labeled as such and not others. This paper utilizes the use of eXplainable-AI (XAI), specifically, Layer-wise Relevance Propagation (LRP) which generates mapping between AI decision and biomarker used by the ophthalmologist whereby enhancing results interpretability and transparency in the disease diagnostic tasks. VGG-16 incorporated with batch normalization and label smoothing was used for the classification tasks whereas LRP was employed to perform the heat-map generation to see if the feature extracted and used by AI was consistent with the experts' biomarkers. Our proposed model obtained a classification accuracy of 77.33%, where 165 out of 266 images were aligned with the ophthalmologist's prediction. Furthermore, the significance of heatmap generation was supported by a one-sample Z-test which revealed that the alignment between AI predictions and expert biomarkers is significantly greater than random, with a 95% confidence interval. | |
| dc.identifier.citation | 22nd International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2025, 2025 | |
| dc.identifier.doi | 10.1109/ECTI-CON64996.2025.11100359 | |
| dc.identifier.other | 2-s2.0-105014375703 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16493 | |
| dc.source | 22nd International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2025 | |
| dc.subject | diabetic retinopathy | |
| dc.subject | eXplainable AI (XAI) | |
| dc.subject | layer-wise relevance propagation (LRP) | |
| dc.subject | VGG | |
| dc.title | Comparing AI Decision-Making with Expert Biomarkers: A Case Study on Diabetic Retinopathy Classification | |
| dc.type | Conference Paper |