Accelerating retinal fundus image classification using artificial neural networks (ANNs) and reconfigurable hardware (FPGA)

Arfan Ghani, Chan H. See, V Sudhakaran, J Ahmad, Raed Abd-Alhameed

Research output: Contribution to journalArticle

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

Diabetic retinopathy (DR) and glaucoma are common eye diseases that aect a blood vessel in the retina and are two of the leading causes of vision loss around the world. Glaucoma is a common eye condition where the optic nerve that connects the eye to the brain becomes damaged, whereas DR is a complication of diabetes caused by high blood sugar levels damaging the back of the eye. In order to produce an accurate and early diagnosis, an extremely high number of retinal images needs to be processed. Given the required computational complexity of image processing algorithms and the need for high-performance architectures, this paper proposes and demonstrates the use of fully parallel field programmable gate arrays (FPGAs) to overcome the burden of real-time
computing in conventional software architectures. The experimental results achieved through software implementation were validated on an FPGA device. The results showed a remarkable improvement in terms of computational speed and power consumption. This paper presents various preprocessing methods to analyse fundus images, which can serve as a diagnostic tool for detection of glaucoma and diabetic retinopathy. In the proposed adaptive thresholding-based preprocessing method, features were selected by calculating the area of the segmented optic disk, which was further classified using a feedforward neural network (NN). The analysis was carried out using feature extraction
through existing methodologies such as adaptive thresholding, histogram and wavelet transform. Results obtained through these methods were quantified to obtain optimum performance in terms of classification accuracy. The proposed hardware implementation outperforms existing methods and oers a significant improvement in terms of computational speed and power consumption.
Original languageEnglish
Article number1522
Number of pages17
JournalElectronics (Switzerland)
Volume8
Issue number12
DOIs
Publication statusPublished - 11 Dec 2019

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Keywords

  • Neural network
  • Machine learning
  • Glaucoma
  • Diabetic Retinopathy
  • Adaptive Threshold
  • FPGA
  • loTs
  • Smart healthcare

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