Comparative Study of Deep Learning ArchitecturesforAutomated Diabetic Retinopathy Grading:Vision Transformer, Swin Transformer, and InceptionResNetV2

References

[1] International Diabetes Federation, “IDF Diabetes Atlas, 10th edition,” 2021. [Online]. Available: https://www.diabetesatlas.org

[2] World Health Organization, “Blindness and vision impairment,” WHO Fact Sheet, 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment

[3] C. P. Wilkinson et al., “Proposed international clinical diabetic retinopa-thy and diabetic macular edema disease severity scales,” Ophthalmology, vol. 110, no. 9, pp. 1677-1682, 2003.

[4] D. S. W. Ting et al., “Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,” JAMA, vol. 318, no. 22, pp. 2211-2223, 2017.

[5] M. D. Abra`moff et al., “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Investigative Ophthalmology & Visual Science, vol. 57, no. 13, pp. 5200-5206, 2016.

[6] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.

[7] A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” in Proc. ICLR, 2021.

[8] Z. Liu et al., “Swin Transformer: Hierarchical vision transformer using shifted windows,” in Proc. IEEE/CVF ICCV, pp. 10012-10022, 2021.

[9] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the impact of residual connections on learning,” in Proc. AAAI, 2017.

[10] V. Gulshan et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus pho-tographs,” JAMA, vol. 316, no. 22, pp. 2402-2410, 2016.

[11] M. D. Abra`moff et al., “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices,” NPJ Digital Medicine, vol. 1, no. 1, pp. 1-8, 2018.

[12] R. Gargeya and T. Leng, “Automated identification of diabetic retinopa-thy using deep learning,” Ophthalmology, vol. 124, no. 7, pp. 962-969, 2017.

[13] B. Graham, “Kaggle diabetic retinopathy detection competition report,” University of Warwick, 2015.

[14] N. Sikder, M. S. Masud, A. K. M. B. Hossain, and M. A. Bhuiyan, “Severity classification of diabetic retinopathy using an ensemble learn-ing algorithm through analyzing retinal images,” Symmetry, vol. 13, no. 4, p. 670, 2021.

[15] S. Qummar et al., “A deep learning ensemble approach for diabetic retinopathy detection,” IEEE Access, vol. 7, pp. 150530-150539, 2019.

[16] X. Wang et al., “Self-attention-based CNNs for diabetic retinopathy grading using fundus image,” in Proc. IEEE ISBI, 2021.

[17] X. Sun, J. Xu, and J. Ma, “Vision transformer for diabetic retinopathy grading,” in Proc. Int. Conf. on Medical Image Analysis and Computer-Aided Diagnosis, 2021.

[18] B. Gheflati and H. Rivaz, “Vision transformers for classification of diabetic retinopathy,” in Proc. IEEE EMBC, pp. 1988-1991, 2022.

[19] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE/CVF ICCV, pp. 618-626, 2017.

[20] A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, “Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks,” in Proc. IEEE WACV, pp. 839-847, 2018.

[21] H. Chefer, S. Gur, and L. Wolf, “Transformer interpretability beyond attention visualization,” in Proc. IEEE/CVF CVPR, pp. 782-791, 2021.

[22] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. ICLR, 2019.

[23] R. Wightman, “PyTorch Image Models (timm),” GitHub, 2019. [Online]. Available: https://github.com/rwightman/pytorch-image-models

[24] Asia Pacific Tele-Ophthalmology Society, “APTOS 2019 Blind-ness Detection,” Kaggle Competition, 2019. [Online]. Available: https://www.kaggle.com/c/aptos2019-blindness-detection

@article{1718689,
      author = {Samir Mulla, Mahamadtohid Naikwadi, Prajwal Khandait, Aditya Sutar, Rajesh Kumar; Uma Gurav},
      title = {Comparative Study of Deep Learning ArchitecturesforAutomated Diabetic Retinopathy Grading:Vision Transformer, Swin Transformer, and InceptionResNetV2},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {527-536},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718689.pdf},
      abstract = {Diabetic Retinopathy (DR) is a vision-threatening complication of diabetes mellitus that progresses silently through five clinically defined severity grades. Timely automated screen-ing is critical to prevent irreversible vision loss, particu-larly in resource-constrained healthcare settings. This paper presents a systematic comparative study of three state-of-the-art deep learning architectures-Vision Transformer (ViT-Base/16), Swin Transformer (swin base patch4 window7 224), and InceptionResNetV2-applied to five-class DR grading on the APTOS 2019 fundus image dataset (3,662 images). All models employ transfer learning from ImageNet-pretrained weights. We analyze each architecture from the perspectives of classification accuracy, per-class F1-score, macro-averaged AUC, GradCAM-based explainability, training dynamics, and parameter efficiency. Our ViT-Base/16 model, fine-tuned end-to-end with AdamW, cosine annealing, and label smoothing, achieves the highest validation accuracy of 85.40% with a macro-averaged F1-score of 0.7247. Swin Transformer achieves 83.20% accuracy, while InceptionResNetV2 achieves 81.40% through two-stage transfer learning. GradCAM visualizations confirm clinically aligned lesion localization across all architectures. This work provides architectural insights for deploying robust DR screening systems in clinical environments.},
      keywords = {Diabetic Retinopathy, Vision Transformer, Swin Transformer, InceptionResNetV2, Transfer Learning, GradCAM, Fundus Image Classification, Deep Learning, Medical Image Analysis},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718689}
  }