Abstract

Diabetic retinopathy (DR) is the leading cause of vision loss in the world, particularly in individuals who have had a 10 or 15 years history of diabetes. Early detection of the disease and automated fundus image analysis techniques would decrease the possibility of vision loss. In this study, a transfer learning method is proposed, which includes a Convolutional Block Attention Module (CBAM) and optimizes the DenseNet121 architecture, resulting in better feature representations, as well as the focus on relevant retinal area discoveries. The model was trained and evaluated using the publicly available Kaggle Diabetic Retinopathy dataset, which contains labeled color fundus images spanning multiple DR severity levels. To ensure a comprehensive evaluation, metrics such as accuracy, precision, recall, and F1-score were employed. We have obtained 97

Keywords

MSC

68T07, 68U10, 92C50

Cite this article

@article{cai-8804,
  author  = {Mankiran Kaur and Puneet Kumar and Anuj Kumar Gupta},
  title   = {Using Transfer Learning with DenseNet121 and Convolutional Block Attention Module (CBAM) for Enhanced Diabetic Retinopathy Classification},
  journal = {Computing and Informatics},
  volume  = {45},
  number  = {3},
  pages   = {601--622},
  year    = {2026},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2026_3_601}
}
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