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Computer vision for eye diseases detection using pre-trained deep learning techniques and raspberry Pi

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dc.contributor.authorAl Naji, A.
dc.contributor.authorKhalid, G.A.
dc.contributor.authorMahmood, M.F.
dc.contributor.authorChahl, J.
dc.date.issued2024
dc.description.abstractEarly diagnosis of eye diseases is very important to prevent visual impairment and guide appropriate treatment methods. This paper presents a unique approach that can detect numerous eye diseases automatically. Initially, this approach used the pre-trained ImageNet models that provides various pre-trained models for training the acquired data. The existing data sets are composed of 645 data images acquired clinically, represented by two groups of subjects as healthy and others holding the proposed eye defect like cataracts, foreign bodies, glaucoma, subconjunctival haemorrhage, and viral conjunctivitis. Followed by comparisons of the pre-trained model's coefficients and prediction performance. Later, the first-class execution model is integrated within the Raspberry Pi staging and the real-time digital camera detection. The evaluation process used the confusion matrix, model accuracy, precision factor, recall coefficient, F1 score, and the Matthews Correlation Coefficient (MCC). Resulting in the performance of these pre-trained ImageNet models used in this study represented by 93% (InceptionResNetV2), 90% (MobileNet), 86% (Residual Network ResNet50), 85% (InceptionV3), 78% (Visual Geometry Group VGG19), and 72% (Neural Architecture Search Network NASNetMobile). The results show that the InceptionResNetV2 achieved the highest performance. This proposed approach shows its efficiency and strength by early detection of the subject's unhealthy eyes through real-time monitoring in the field of ophthalmology.
dc.identifier.citationThe Journal of Engineering, 2024; 2024(7, article no. e12410):1-10
dc.identifier.doi10.1049/tje2.12410
dc.identifier.issn2051-3305
dc.identifier.issn2051-3305
dc.identifier.urihttps://hdl.handle.net/11541.2/39297
dc.language.isoen
dc.publisherThe Institution of Engineering and Technology
dc.rightsCopyright 2024 The Author(s). The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1049/tje2.12410
dc.subjectartificial intelligence
dc.subjectbiomedical signal and image processing
dc.titleComputer vision for eye diseases detection using pre-trained deep learning techniques and raspberry Pi
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9916878516401831

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