TY  - JOUR
AU  - Khalid, Muhammad Imran
AU  - Ahmad, Israr
AU  - Saleem, Sohaibe
AU  - Hussain, Altaf
AU  - Hussain, Syed Akif
AU  - Waghan, Atif Ali
AU  - Khan, Muzamil
PY  - 2025
DA  - 2025/12/18
TI  - Interpretable Deep Learning for Diabetic Retinopathy Grading using Regression Activation Maps
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 1
IS  - 4
SP  - 196
EP  - 209
DO  - 10.62762/JIAP.2025.346328
UR  - https://www.icck.org/article/abs/JIAP.2025.346328
KW  - diabetic retinopathy
KW  - deep learning
KW  - explainable AI
KW  - regression activation maps (RAM)
KW  - global average pooling
AB  - The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts, their black-box nature erodes clinical trust. To harmonize accuracy with interpretability, this paper proposes a novel CNN architecture that reformulates DR grading as a regression task. By substituting traditional dense layers with a Global Average Pooling (GAP) layer, our approach substantially reduces model complexity and training time while enabling the generation of Regression Activation Maps (RAMs). These RAMs deliver visual explanations by precisely highlighting the pathological regions that underpin the model's predictions. Evaluated on the Kaggle Diabetic Retinopathy Detection dataset, our model—through the replacement of dense layers with Global Average Pooling—markedly lowers model complexity while delivering diagnostic performance on par with baseline models employing fully-connected layers. The resulting system provides a simpler, more precise, and transparent alternative for automated medical screening, directly associating predictions with clinically relevant features.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khalid2025Interpreta,
  author = {Muhammad Imran Khalid and Israr Ahmad and Sohaibe Saleem and Altaf Hussain and Syed Akif Hussain and Atif Ali Waghan and Muzamil Khan},
  title = {Interpretable Deep Learning for Diabetic Retinopathy Grading using Regression Activation Maps},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2025},
  volume = {1},
  number = {4},
  pages = {196-209},
  doi = {10.62762/JIAP.2025.346328},
  url = {https://www.icck.org/article/abs/JIAP.2025.346328},
  abstract = {The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts, their black-box nature erodes clinical trust. To harmonize accuracy with interpretability, this paper proposes a novel CNN architecture that reformulates DR grading as a regression task. By substituting traditional dense layers with a Global Average Pooling (GAP) layer, our approach substantially reduces model complexity and training time while enabling the generation of Regression Activation Maps (RAMs). These RAMs deliver visual explanations by precisely highlighting the pathological regions that underpin the model's predictions. Evaluated on the Kaggle Diabetic Retinopathy Detection dataset, our model—through the replacement of dense layers with Global Average Pooling—markedly lowers model complexity while delivering diagnostic performance on par with baseline models employing fully-connected layers. The resulting system provides a simpler, more precise, and transparent alternative for automated medical screening, directly associating predictions with clinically relevant features.},
  keywords = {diabetic retinopathy, deep learning, explainable AI, regression activation maps (RAM), global average pooling},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}