Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and transfer learning models, classifying images into five DR stages. Python libraries like TensorFlow facilitate data preprocessing, model development, and evaluation. Rigorous cross-validation and hyperparameter tuning optimized model accuracy, demonstrating their effectiveness in early risk identification, personalized healthcare recommendations, and improving patient outcomes.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5868 |
| Date | 01 May 2024 |
| Creators | Olatunji, Aishat |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
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