Implementation of a Deep Learning-Based Approach for Diabetic Retinopathy Diagnosis and Severity Grading Using ResNet-50 Architecture

References

[1] M. R. Islam, S. Akter, and T. Chakraborty, “Attention-Enhanced CNN-LSTM Architecture for Crop Yield Prediction Using Environmental and Remote Sensing Data,” IEEE Access, vol. 12, pp. 145233–145245, 2024.

[2] Y. Zhang, K. Li, and L. Sun, “A Transformer-Based Spatiotemporal Learning Framework for Multi-Regional Crop Production Forecasting,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–14, 2024.

[3] P. Kumar, S. Meena, A. Raj, and R. K. Mohan, “Hybrid Deep Neural Networks for Precision Agriculture: A Study on Crop Prediction with Soil and Climate Variables,” IEEE Sensors Journal, vol. 24, no. 3, pp. 2522–2535, 2024.

[4] L. Hernandez, J. Paulo, and V. Santos, “Optimized Random Forest and XGBoost Ensemble for Multivariate Crop Growth Prediction,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 44–58, 2024.

[5] A. Chaudhary and M. Yadav, “Deep Convolutional Networks for Satellite-Based Crop Productivity Estimation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 789–802, 2024.

[6] H. J. Park and S. W. Lee, “A Data-Driven Crop Health Monitoring Model Using Multispectral Drone Imagery,” IEEE Access, vol. 11, pp. 178450–178463, 2023.

[7] R. M. Silva and T. Higuchi, “Long Short-Term Memory Networks for Seasonal Agricultural Forecasting,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 554–566, 2023.

[8] S. Patel and D. Shah, “Climate-Adaptive Machine Learning Models for Agricultural Productivity Assessment,” IEEE Transactions on Sustainable Computing, vol. 8, no. 4, pp. 1022–1033, 2023.

[9] N. Noor, M. Khalid, and R. Ahmed, “Deep Ensemble Learning for Multimodal Crop Prediction Integrating Soil, Weather, and Vegetation Indices,” IEEE Access, vol. 11, pp. 161201–161214, 2023.

[10] F. Rossi and G. Bianchi, “IoT-Enabled Predictive Agriculture Using Neural Networks and Cloud Platforms,” IEEE Internet of Things Journal, vol. 10, no. 6, pp. 5490–5502, 2023.

[11] M. Abebe and T. Gebre, “A Feature Fusion Strategy for Enhancing Crop Yield Prediction Accuracy,” IEEE Transactions on Knowledge and Data Engineering, 2023.

[12] L. Singh and V. Jadhav, “Satellite Image Segmentation for Agricultural Zones Using U-Net,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023.

[13] S. Raut and P. Kulkarni, “Machine Learning-Based Soil Fertility Classification for Smart Agriculture,” IEEE Access, vol. 11, pp. 142300–142311, 2023.

[14] J. Thomas and M. Christian, “A Comparative Study of ML Algorithms for Predicting Wheat Yield Using Multiyear Field Data,” IEEE Transactions on Agriculture and Food Engineering, vol. 2, no. 1, pp. 77–89, 2023.

[15] A. P. Hassan and K. Al-Rashid, “A Unified Crop Monitoring Model Using Time-Series Vegetation Indexes,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–12, 2023.

[16] P. Wang and C. Liu, “Spatial-Temporal Prediction of Corn Yield Through Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 9877–9890, 2023.

[17] V. Menon and A. Gopal, “Hybrid ML Framework Combining SAR and Optical Imagery for Agricultural Mapping,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3485–3497, 2023.

[18] S. Ojha and K. Sen, “A Smart Agriculture Recommendation System Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 109654–109667, 2023.

[19] T. Abdullah and M. Rahman, “A Sustainable Deep Learning Model for Crop Yield Prediction Under Changing Climate,” IEEE Transactions on Sustainable Computing, 2023.

[20] L. Torres and H. Kim, “CNN-Based Soil Texture Identification Using High-Resolution Field Images,” IEEE Sensors Journal, vol. 23, no. 8, pp. 9450–9461, 2023.

@article{1717596,
      author = {Jyoti Tulshiram Vairal, Dr. M. A. Wakchaure},
      title = {Implementation of a Deep Learning-Based Approach for Diabetic Retinopathy Diagnosis and Severity Grading Using ResNet-50 Architecture},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1337-1344},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717596.pdf},
      abstract = {One of the most prevalent causes of blindness that can be prevented is diabetic retinopathy (DR). Because of this, automated screening tools that can identify early disease indicators in retinal fundus images are crucial. The severity levels of DR may now be correctly classified thanks to recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs). With an emphasis on the ResNet-50 and Retinopathy Severity Grading (RSG-Net) designs, this paper examines the most effective methods for identifying DR. The study demonstrates how DR grading may be made more dependable across several datasets, including the APTOS 2019 Blindness Detection dataset, by preprocessing photos, adding new data, and utilising transfer learning. Several stages of DR, from No DR to Proliferative DR, are examined to determine how well current CNN models can diagnose. The benefits and drawbacks of binary and multi-class classification methods, as well as their applicability in the clinic, are also thoroughly examined in this work. This study examines CNN-based techniques for early detection of DR and discusses how they can support widespread, real-time eye screening.},
      keywords = {Diabetic Retinopathy, ResNet-50, RSG-Net, Fundus Images, Deep Learning, Convolutional Neural Network, DR Classification, APTOS 2019, Medical Image Analysis, Automated Screening.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717596}
  }