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

Author:

Subramanian Malliga1,Kumar M. Sandeep2,Sathishkumar V. E.3,Prabhu Jayagopal2ORCID,Karthick Alagar4,Ganesh S. Sankar5ORCID,Meem Mahseena Akter6ORCID

Affiliation:

1. Department of Computer Science Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India

2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India

3. Department of Industrial Engineering, Hanyang University, Seoul, Republic of Korea

4. Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India

5. Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India

6. Department of Electrical and Electronic Engineering, Daffodil International University, Ashulia, Savar, Dhaka 1207, Bangladesh

Abstract

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.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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