Article: Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2024 Vol.16 No.6 pp.584 - 593 Abstract: The World Health Organization (WHO) recognises that diabetic retinopathy (DR) is one of the rising healthcare problems in the world which leads to vision loss if left untreated. In this paper, an automated computer aided diagnosis (CAD) system for DR is implemented based on EfficientNet. Initially, image pre-processing is performed by smoothing it with median filter and converting into grey scale image. Then size, colour and shape normalisation are carried out. To increase the volume and to solve the data imbalance issue flipping, rotation, zooming and distortion operations are performed. The CAD system diagnoses the severity levels of DR from fundus images by exploring feature extraction based on EfficientNet B0 model and classification with XGBoost classifier. It is experimented with Indian Diabetic Retinopathy Image Dataset (IDRiD) and experimental result reveals that, the combination of EfficientNet B0 and XGBoost produces better classification accuracy when compared with other convolutional neural network (CNN) models. Inderscience Publishers - linking academia, business and industry through research

Title: Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet

Authors: S. Sathiya Devi; K. Vignesh; V. Raguvaran

Addresses: Department of Information Technology, University College of Engineering, BIT Campus, Trichirappalli – 24, Tamil Nadu, India ' Department of Information Technology, University College of Engineering, BIT Campus, Trichirappalli – 24, Tamil Nadu, India ' Department of Information Technology, University College of Engineering, BIT Campus, Trichirappalli – 24, Tamil Nadu, India

Abstract: The World Health Organization (WHO) recognises that diabetic retinopathy (DR) is one of the rising healthcare problems in the world which leads to vision loss if left untreated. In this paper, an automated computer aided diagnosis (CAD) system for DR is implemented based on EfficientNet. Initially, image pre-processing is performed by smoothing it with median filter and converting into grey scale image. Then size, colour and shape normalisation are carried out. To increase the volume and to solve the data imbalance issue flipping, rotation, zooming and distortion operations are performed. The CAD system diagnoses the severity levels of DR from fundus images by exploring feature extraction based on EfficientNet B0 model and classification with XGBoost classifier. It is experimented with Indian Diabetic Retinopathy Image Dataset (IDRiD) and experimental result reveals that, the combination of EfficientNet B0 and XGBoost produces better classification accuracy when compared with other convolutional neural network (CNN) models.

Keywords: diabetic retinopathy; EfficientNet B0; XGBoost classifier; computer aided diagnosis system; healthcare; convolutional neural network; CNN.

DOI: 10.1504/IJMEI.2024.141796

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.6, pp.584 - 593

Received: 16 Feb 2022
Accepted: 13 Jul 2022
Published online: 02 Oct 2024 *

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Title: Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet

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