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DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases

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A Correction to this article was published on 09 April 2021

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Abstract

Artificial intelligence has the potential to revolutionize disease diagnosis, classification, and identification. However, the implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. This study presents a diagnostic tool based on a deep-learning framework for four-class classification of ocular diseases by automatically detecting diabetic macular edema, drusen, choroidal neovascularization, and normal images in optical coherence tomography (OCT) scans of the human eye. The proposed framework utilizes OCT images of the retina and analyses using three different convolution neural network (CNN) models (five, seven, and nine layers) to identify the various retinal layers extracting useful information, observe any new deviations, and predict the multiple eye deformities. The framework utilizes OCT images of the retina, which are preprocessed and processed for noise removal, contrast enhancements, contour-based edge, and detection of retinal layer extraction. This image dataset is analyzed using three different CNN models (of five, seven, and nine layers) to identify the four ocular pathologies. Results obtained from the experimental testing confirm that our model has excellently performed with 0.965 classification accuracy, 0.960 sensitivity, and 0.986 specificities compared with the manual ophthalmological diagnosis.

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Acknowledgements

This study was supported by Taif University Researchers Supporting Project (number: TURSP-2020/10), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, New Delhi, India

    Akash Tayal, Jivansha Gupta & Khyati Bisht

  2. Gautam Buddha University, Greater Noida, India

    Jivansha Gupta & Arun Solanki

  3. Duy Tan University, Da Nang, Viet Nam

    Anand Nayyar

  4. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Mehedi Masud

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  1. Akash Tayal
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Correspondence to Anand Nayyar.

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Tayal, A., Gupta, J., Solanki, A. et al. DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems 28, 1417–1438 (2022). https://doi.org/10.1007/s00530-021-00769-7

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