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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Unveiling Advanced AI: Revolutionizing Diabetic Eye Care with Graph-Based Insights |
| Authors Name: | Jayashakthi R , Mrs D.Malathi , Abi A , Swathi E , Vichitra V |
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IJRTI_203679
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| Published Paper Id: | IJRTI2505205 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | In this study, we present DiaNet, a novel multi-stage Convolutional Neural Network (CNN)-based model, designed to classify individuals as diabetic or non-diabetic solely based on their retinal fundus images. By leveraging transfer learning and fine-tuning CNN architectures across multiple datasets, our model effectively extracts discriminative features from retinal images, achieving an accuracy exceeding 94%. The methodology involves a multi-stage training approach, integrating retinal images from the QBB and EyePACS datasets to enhance the model’s robustness and generalizability. Furthermore, explainability techniques such as activation mapping reveal the specific retinal regions that contribute to classification decisions, corroborated by expert ophthalmologists. A comparative evaluation of DiaNet against conventional biomarker-based models highlights the ability of retinal imaging to serve as a reliable and scalable diagnostic modality. Additionally, findings suggest that subtle vascular anomalies in the retina may act as early prognostic indicators, not only for diabetes but for comorbidities like hypertension and ischemic heart disease. Given the cost-effectiveness and non-invasive nature of retinal imaging, our approach aligns with global health initiatives, including those advocated by the International Diabetes Federation (IDF) and the World Health Organization (WHO), aiming for low-cost, accessible diabetes screening solutions. The integration of DiaNet into clinical workflows has the potential to revolutionize diabetes screening, reducing physician workload in high-income nations and enhancing mass screening efficiency in low- and middle-income countries. This research underscores the importance of image-based AI-driven diagnostics and lays the groundwork for future advancements in automated medical imaging applications |
| Keywords: | Deep Learning; DiaNet; Convolutional Neural Network (CNN); Medical Image Classification; Retinal Fundus Images; Topological Feature Extraction; Graph Neural Networks (GNN); Neo-vessel Synthesis; Data Augmentation; Disease Severity Detection; Automated Diagnosis; Image Preprocessing; Multiclass Classification; Proliferative Diabetic Retinopathy. |
| Cite Article: | "Unveiling Advanced AI: Revolutionizing Diabetic Eye Care with Graph-Based Insights", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c38-c43, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505205.pdf |
| Downloads: | 000248 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2505205
Registration ID:203679
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c38-c43 Country: Cuddalore, Tamil Nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505205 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505205 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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