Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images
Esraa Hassan,
Samir Elmougy,
Mai R. Ibraheem,
M. Shamim Hossain,
Khalid AlMutib,
Ahmed Ghoneim,
Salman A. AlQahtani,
Fatma M. Talaat
Affiliations
Esraa Hassan
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Samir Elmougy
Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
Mai R. Ibraheem
Department of Information Technology, Faculty of Computers and information, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
M. Shamim Hossain
Research Chair of Pervasive and Mobile Computing, Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Khalid AlMutib
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11574, Saudi Arabia
Ahmed Ghoneim
Research Chair of Pervasive and Mobile Computing, Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Salman A. AlQahtani
Research Chair of Pervasive and Mobile Computing, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11574, Saudi Arabia
Fatma M. Talaat
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
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.
