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Efficient hybrid approach to segment and classify exudates for DR prediction

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Abstract

Diabetic retinopathy (DR) is initiated due to the severity of diabetes which can finally lead to an incurable blindness. It is a significant reason for optical damage that may cause blindness permanently. There are no main symptoms of DR appearing initially but its quantity and severity rises with the passage of time. Initial screening and diagnosis of DR may help to stop vision loss. Exudates (EXs) are one of the primary clinical symptoms of DR. In this manuscript, a computerized technique is proposed for DR detection based on EXs. The proposed system is consisting of four major steps. The first step is enhancement of region of interest using median filter and adaptive contrast enhancement method. After that, local variance and global threshold methods are utilized for candidate lesions segmentation. Moreover, texture features with multiple classifiers are applied for classification. The proposed method is evaluated in terms of sensitivity, specificity, accuracy and area under curve on DIARETDB1, MESSIDOR and local data sets.

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Acknowledgements

This work is supported by Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan. We are thankful to COMSATS for providing a strong research platform, fully equipped labs and other research facilities to make this work possible.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

    Muhammad Sharif & Mussarat Yasmin

  2. Department of Computer Science, University of Wah, Wah Cantt, Pakistan

    Javeria Amin

  3. College of Computer and Information Systems, Al Yamamah University, Riyadh, Saudi Arabia

    Amjad Rehman

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  1. Muhammad Sharif
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Correspondence to Mussarat Yasmin.

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Sharif, M., Amin, J., Yasmin, M. et al. Efficient hybrid approach to segment and classify exudates for DR prediction. Multimed Tools Appl 79, 11107–11123 (2020). https://doi.org/10.1007/s11042-018-6901-9

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