Title: Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images

Authors: D. Selvathi; K. Suganya; M. Menaka; B. Venkatraman

Addresses: Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi – 626005, Virudhunagar District, Tamil Nadu, India ' Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi – 626005, Virudhunagar District, Tamil Nadu, India ' Safety, Quality and Resource Management Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam – 603102, Chennai, Tamil Nadu, India ' Safety, Quality and Resource Management Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam – 603102, Chennai, Tamil Nadu, India

Abstract: Infrared thermography which is non-contact and non-invasive technique is widely accepted as a medical diagnostic tool. Thermal images are processed for abnormality detection and quantification. It has been used in the diagnosis of dry eye, meibomian gland dysfunction, thyroid eye disease and glaucoma. Diabetic eye disease (DED) detection using thermal images is an absolutely new attempt. The early detection of the occurrence of DED can be very helpful for clinical treatment. In this paper, we are attempting towards finding an automatic way to classify DEDs in thermal images using a deep learning-based convolutional neural network (CNN) methodology. The sensitivity of 92.30%, specificity of 98.46%, and accuracy of 95.38% on testing dataset with reference to expert's ground truth results are obtained. The results attained evidently exhibit that the thermal imaging is promising modality and proposed deep learning method is capable for automatic diagnosis of diabetic eye disease classification.

Keywords: convolutional neural network; CNN; diabetic eye disease; DED; thermal imaging; classification; ground truth; accuracy; diagnosis; RGB; testing dataset; deep learning.

DOI: 10.1504/IJRIS.2021.114640

International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.2, pp.106 - 114

Received: 12 Aug 2019
Accepted: 03 Apr 2020

Published online: 30 Mar 2021 *

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