Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN

Imran Qureshi; Qaisar Abbas; Junhua Yan; Ayyaz Hussain; Kashif Shaheed; Abdul Rauf Baig

doi:10.3390/app122312086

Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN

Abstract

Hypertensive retinopathy (HR) is a retinal disorder, linked to high blood pressure. The incidence of HR-eye illness is directly related to the severity and duration of hypertension. It is critical to identify and analyze HR at an early stage to avoid blindness. There are presently only a few computer-aided systems (CADx) designed to recognize HR. Instead, those systems concentrated on collecting features from many retinopathy-related HR lesions and then classifying them using traditional machine learning algorithms. Consequently, those CADx systems required complicated image processing methods and domain-expert knowledge. To address these issues, a new CAD-HR system is proposed to advance depth-wise separable CNN (DSC) with residual connection and a linear support vector machine (LSVM). Initially, the data augmentation approach is used on retina graphics to enhance the size of the datasets. Afterward, this DSC approach is applied to retinal images to extract robust features. The retinal samples are then classified as either HR or non-HR using an LSVM classifier as the final step. The statistical investigation of 9500 retinograph images from two publicly available and one private source is undertaken to assess the accuracy. Several experimental results demonstrate that the CAD-HR model requires less computational time and fewer parameters to categorize HR. On average, the CAD-HR achieved a sensitivity (SE) of 94%, specificity (SP) of 96%, accuracy (ACC) of 95% and area under the receiver operating curve (AUC) of 0.96. This confirms that the CAD-HR system can be used to correctly diagnose HR.

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Authors (6)

Imran Qureshi
- Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology (MCS-NUST), Islamabad 44000, Pakistan
Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Junhua Yan
- Key Laboratory of Space Photoelectric Detection and Perception, Ministry of Industry and Information Technology and College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
Kashif Shaheed
Abdul Rauf Baig
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan

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Category:

Articles

Type:

artykuły w czasopismach

Published in:

Applied Sciences-Basel no. 12,
ISSN: 2076-3417

Language:

English

Publication year:

2022

Bibliographic description:

Qureshi I., Abbas Q., Yan J., Hussain A., Shaheed K., Baig A. R.: Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN// Applied Sciences-Basel -Vol. 12,iss. 23 (2022), s.12086-

DOI:

10.3390/app122312086

Sources of funding:

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this work through Research Partnership Program no. RP-21-07-04.

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