Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 8014979 - 15
• Main Authors: Subramanian, Malliga, Kumar, M. Sandeep, Sathishkumar, V. E., Prabhu, Jayagopal, Karthick, Alagar, Ganesh, S. Sankar, Meem, Mahseena Akter
• Format: Journal Article
• Language: English
• Published: United States Hindawi 15-04-2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Abnormalities; Algorithms; Artificial neural networks; Bayes Theorem; Bayesian analysis; Blindness; Classification; Datasets; Deep Learning; Diabetes; Diabetic retinopathy; Disease; Edema; Glaucoma; Humans; Image classification; Machine learning; Macular degeneration; Mathematical models; Medical imaging; Medical imaging equipment; Medical personnel; Neural networks; Neural Networks, Computer; Optical Coherence Tomography; Optimization; Public health; Retina; Retinal diseases; Retinal Diseases - diagnostic imaging; Tomography; Tomography, Optical Coherence - methods; Transfer learning; India
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/8014979

Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.