International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
ENHANCED DETECTION OF DIABETIC RETINOPATHY THROUGH MULTIRETINAL DISEASE PREDICTION FRAMEWORK SHANMUGA PRIYA R1, JEEVA M2 1M. Tech Student, Department of Computer Science and Engineering, PRIST University, Thanjavur, Tamil Nadu,
India.
2M.E., Assistant Professor, Department of Computer Science and Engineering, PRIST University, Thanjavur, Tamil
Nadu, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
ABSTRACT- Retinal diseases (e.g. diabetic retinopathy,
Recent developments in artificial intelligence, especially in deep learning, have produced new tools in medical diagnostics. In particular, convolutional neural networks (CNNs) have advanced the state of the art in image-based analysis. There is substantial evidence of their effectiveness across a variety of medical domains including ophthalmology. They have quickly been adopted by the community in computing the detection, and classification of retinal disorders. This research tackles the challenge of an automated diagnostic system, which applies CNNs to retinal images in order to predict retinal diseases. This work proposes a methodology using well-annotated, diverse retinal image datasets to create a CNN model, to accurately categorize and detect the presence, in addition to the severity, of a variety of retinal diseases. CNNs provide so many benefits in this setting, for example, CNNs can help to learn more complex features and patterns that are not known to expert human raters, and they allow for faster diagnoses, which can provide timely medical treatment. In addition, automated systems such as this, could help address shortages of trained ophthalmologists, notably in underserved areas.
age-related macular degeneration, glaucoma, and retinal detachment) constitute some of the foremost causes of vision loss and blindness across the world. The early and accurate diagnosis of diseases in the retina remains paramount to effective treatment, and hence patient outcomes. This research proposes a deep learning approach that uses convolutional neural networks (CNNs) to identify and classify different eye disorders. A large dataset of retinal images labelled with sickness categories is used to train the classifier. To ensure consistency in input for improved feature extraction, pre-processing is applied to the retinal images. Data augmentation methods are also implemented to improve dataset quality and prevent overfitting. Convolutional layers for feature extraction, pooling layers for image down sampling, and fully linked layers for classification comprise the CNN architecture. The labelled dataset is used to train the model through supervised learning techniques. Validation loss is used to closely monitor performance in order to avoid overfitting. Model evaluation is performed on a separate test dataset, and results are reported as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). In addition, post-processing methods are used to filter lowconfidence predictions to improve the reliability of the system for field performance in clinical scenarios.
Figure 1 shows a retinal image with the common characteristic areas of the various retinal diseases marked clearly.
Key Words: AUC-ROC, Convolutional Neural Network, Data Augmentation, Image Preprocessing, Retinal Diseases, Supervised Learning, Vision Loss
I. INTRODUCITON The human retina is a fragile and complex tissue, responsible for transducing incoming light stimuli, into neural signals, which is how we perceive the visual world. Unfortunately, there are many diseases of the retina (i.e. diabetic retinopathy, age related macular degeneration, glaucoma, retinal detachments), that can affect the function of the retina, and if not diagnosed or treated, they can lead to irreversible visual impairment, or complete vision loss. Therefore, timely and multiple ocular disease-related diagnoses are critical to effective treatment, and ultimately better patient outcomes.
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Figure 1: Normal and Diabetic retinal images
2. RELATED WORK Hasan, Md Kamrul, et al. [1] developed an ensemble method that combines different machine learning classifiers to predict diabetes more accurately. The
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