DIABETIC RETINOPATHY LESION DETECTION FROM MULTISPECTRAL RETINAL IMAGES THROUGH NEURAL NETWORK

Authors

  • G M Atiqur Rahaman Computational Color and Spectral Image Analysis Lab, Computer Science & Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
  • S.M. Riasat Ali Computational Color and Spectral Image Analysis Lab, Computer Science & Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
  • Soma Paul Computational Color and Spectral Image Analysis Lab, Computer Science & Engineering Discipline, Khulna University, Khulna 9208, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2020.17.1and2.2001-E

Keywords:

Detection, Classification, Multispectral Image, Neural network, Diabetic Retinopathy

Abstract

Diabetic Retinopathy (DR) is one of the fastest growing dysfunctions of human retina. Significant research has been conducted using RGB fundus imaging for automatic detection of retinal lesions affected by DR. However, due to only three imaging bands, the accuracy from RGB fundus images is unlikely to improve any further. In contrast to RGB imaging, multispectral imaging has the key advantage of multiple narrow wavelength bands that can be used as spectral features to improve the detection accuracy. Nevertheless, the inter and intra-retinal variation of color, contrast, and illumination is a challenge to process the multispectral images. In this study, a complete framework is proposed to develop and evaluate methods for automatic detection of DR lesions. A multispectral retinal image database, DIARETSPECDB1, is investigated in order to detect the most common DRs such as Microaneurysms (MA), Hard Exudates (HE) and Hemorrhages (HEM). The reflectance values of the spectral bands are used as features of a three-layer basic neural network (NN) to determine the baseline performance of multispectral data instead of any advanced model. According to the results, the model outperforms existing technique producing overall accuracy 94.5%, and the obtained specificity/sensitivity is 0.95/0.89, 0.97/0.89, and 0.88/0.84 for MAs, HEs and HEMs, respectively.

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Published

24-12-2020

How to Cite

[1]
G. M. A. . Rahaman, S. R. . Ali, and S. . Paul, “DIABETIC RETINOPATHY LESION DETECTION FROM MULTISPECTRAL RETINAL IMAGES THROUGH NEURAL NETWORK”, Khulna Univ. Stud., pp. 41–55, Dec. 2020.

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Section

Science & Engineering

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