Open Access

ARTICLE

Intelligent and Integrated Framework for Exudate Detection in Retinal Fundus Images

Muhammad Shujaat¹, Numan Aslam¹, Iram Noreen¹, Muhammad Khurram Ehsan^1,*, Muhammad Aasim Qureshi¹, Aasim Ali¹, Neelma Naz², Imtisal Qadeer³

1 Bahria University, Lahore Campus, Lahore, 54000, Pakistan
2 Seecs, National University of Sciences and Technology, Islamabad, 44000, Pakistan
3 Ind. Researcher, Neckarstrasse 244, PLZ: 70190, Stuttgart, Germany

* Corresponding Author: Muhammad Khurram Ehsan. Email:

Intelligent Automation & Soft Computing 2021, 30(2), 663-672. https://doi.org/10.32604/iasc.2021.019194

Received 04 April 2021; Accepted 05 May 2021; Issue published 11 August 2021

Abstract

Diabetic Retinopathy (DR) is a disease of the retina caused by diabetes. The existence of exudates in the retina is the primary visible sign of DR. Early exudate detection can prevent patients from the severe conditions of DR An intelligent framework is proposed that serves two purposes. First, it highlights the features of exudate from fundus images using an image processing approach. Afterwards, the enhanced features are used as input to train Alexnet for the detection of exudates. The proposed framework is comprised on three stages that include pre-processing, image segmentation, and classification. During the pre-processing stage, image quality is enhanced using contrast enhancement, median filtering, and removal of Optic Disc (OD). In the segmentation stage, image segmentation is performed using morphological operations. During the classification phase, these segmented images are then applied as input to Alexnet for fine-tuning of features and classification. A new data set of fundus images is also developed using data from a local hospital. CNN classifier Alexnet is applied on a newly developed local data set of fundus retinal images with 96.6% accuracy and on a benchmark public research data set DIARETDB1 with 98.88% accuracy. The statistical evaluation and comparative analysis of the proposed approach with existing state-of-the-art techniques validate that the proposed framework is robust and outperforms other methods for early detection of exudates in the fundus.

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Keywords

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