Abstract
Background: Prevalent, preventable or treatable causes of blindness include Diabetic Retinopathy (DR), Glaucoma, Cataract and Optic Nerve Head Swelling. Community screening camps are routinely conducted to mainly screen for these diseases/conditions. This is crucial for maintaining good vision and preventing irreversible damage that can lead to blindness. Screening is for early diagnosis, to ensure further investigations and prompt treatment to save vision. The use of intelligent systems to assist the healthcare professionals can help speed up the diagnostic process.
Methodology: Retinal fundus images (RFI) can be used to diagnose several ocular diseases including diabetic retinopathy, glaucoma, cataract and optic nerve head swelling. After preprocessing, the RFI can be classified by trained Neural Networks to detect these diseases. We have curated a dataset with 5600 images in 5 classes and used it to train, validate and test our Ocular Disease Detection System (ODDS). ODDS uses the EfficientNet-B3 model for image classification.
Results: The ODDS performs well with a testing accuracy of 93%. Other performance metrics including precision, recall and F1-scores are also high for all the classes.
Conclusion: The Ocular Disease Detection System (ODDS) will be very useful in community screening programmes and in remote or rural regions. Although the outcomes achieved are highly promising, augmenting the amount of training data from a range of different sources would make the system robust and enhance the practical applicability of the system. The possibility of utilising smartphones equipped with an appropriate lens assembly to record RFI should be explored. This will reduce costs and increase the accessibility of the system.
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Thiagarajan, P., Suguna, M. (2023). Deep Learning Ocular Disease Detection System (ODDS). In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_21
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