Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therap...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5393
Main Authors: Hassan, Esraa, Elmougy, Samir, Ibraheem, Mai R., Hossain, M. Shamim, AlMutib, Khalid, Ghoneim, Ahmed, AlQahtani, Salman A., Talaat, Fatma M.
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.06.2023
MDPI
Subjects:
Accuracy
Algorithms
artificial intelligence
Biometrics
Blood vessels
Classification
Datasets
Decision trees
Deep Learning
Diabetes
Diagnosis
Eye diseases
Health aspects
Intraocular lenses
Macular degeneration
Myopia
Neural Networks, Computer
optical coherence tomography (OCT)
optical sensor technologies
Predictive Value of Tests
Retina
Retina - diagnostic imaging
Sensors
Signal processing
Surgery
Tomography
Tomography, Optical Coherence - methods
Type 2 diabetes
Visual impairment
Saudi Arabia
deep learning
optical sensor technologies
artificial intelligence
optical coherence tomography (OCT)
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23125393