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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences
Kumar Narayanan
Vels Institute of Science, Technology and Advanced Studies
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Lesion-semantic token guided transformer fusion for stage-adaptive diabetic retinopathy grading and screening Murali Gujjula; Kumar Narayanan
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1112-1122

Abstract

Diabetic retinopathy (DR) screening from retinal fundus images is still difficult because of the heterogeneity of lesions, significant class imbalance, and low reliability of existing deep learning algorithms owing to their generic feature pooling and passive fusion mechanisms. To overcome such limitations, this study presents a hybrid model called HandEffiTrans-DR, which combines deep retinal representations with handcrafted lesion descriptors via the directed transformer-based attention mechanism. First, a lesion-prior-guided token squeezer (LPG-TS) module is proposed in the work, which is capable of creating lesion-specific visual tokens via integrating prior knowledge about pathology into the tokenization procedure. Moreover, a directed transformer-based fusion (T→V) strategy allows for actively fusing the handcrafted lesion features with visual representations. The introduced framework is assessed with a stratified five-fold cross-validation protocol. Its average classification accuracy surpasses 97%, while the macro-F1 score is higher than 0.97 and Cohen's kappa is greater than 0.95, indicating good agreement despite the presence of class imbalance. The probability reliability is measured in terms of expected calibration error (ECE) and Brier scores and shows well-calibrated confidence. Further validation is performed on two external datasets, where consistent performance with minor decline is shown.
Transforming diabetic retinopathy detection with deep learning and handcrafted feature fusion G. Murali; Kumar Narayanan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1589-1600

Abstract

The task of early and accurate screening of diabetic retinopathy (DR) from fundus images is affected by the presence of class imbalance, small grade differences, and the lack of interpretability of deep learning models. This paper proposes HandEffiTrans-DR, a hybrid model that represents lesion statistics as semantic tokens and combines them with EfficientNetB0 deep features using a multi-head transformer encoder. A stepwise grading approach for five-class, three-class, and binary classification allows for progressive improvement of decision boundaries while keeping the grading relevant. Hand-engineered haemorrhage features, tested for quantitative trend and lesion map validation, help focus attention on pathology-defined areas during fusion. Experiments on the Kaggle EyePACS dataset report accuracies of 0.81, 0.88, and 0.98 for the three stages, with an area under the curve (AUC) of 1.00 for binary screening. Baseline comparisons clearly indicate the effectiveness of the proposed hybrid model over hand engineered and Convolutional neural network (CNN)-based models. Cross dataset testing on Asia Pacific Tele-Ophthalmology Society (APTOS) and an independent EyePACS dataset sustains accuracy above 0.95, ensuring the effectiveness of the proposed model.
Co-Authors G. Murali Murali Gujjula