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Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets

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Abstract

Glaucoma damages the optical nerve, which sends visual pictures to the brain, and results in irreversible vision loss. This chronic infection is the second leading cause of permanent blindness across the world and worsens the purpose of life if not cured at an early stage. Traditional ways of diagnosing glaucoma, however, rely on heavy equipment and highly trained personnel, making it impossible to assess huge populations of individuals. This results in high costs and lengthy wait times. As a result, new methods for diagnosing glaucoma that do not exacerbate these problems need to be investigated. Previously, to detect glaucoma through artificial intelligence, features were extracted manually, which not only consumes a lot of time but is also a tedious task to perform and there is a chance of intra-observer variability. Now, deep learning (DL) techniques can be used to extract features automatically, which was not possible in the traditional methods. In view of the multiple associated problems like limited labeled data, difficulty and cost incurred in building glaucoma fundus photographic datasets, and special hardware requirements, this study assessed the performance of a DL model (s) which are trained in detecting glaucoma from fundus pictures and methods. The objective is to present a versatile DL model which should generate auspicious performance across multiple datasets to meet real-life scenarios instead of generating specific dataset performance and, along with it, take care of these coupled problems. Diverse deep learning techniques are investigated in this empirical study to categorise the fundus images into two classes: normal and glaucomatous. On all these models, fine-tuning with transfer learning is also performed. Three different publicly available benchmark datasets (ACRIMA, ORIGA, and HRF) were used for training and validation. The models were tested not only on DRISHTI-GS (a public dataset) and a private dataset but also on twelve combinations of these five datasets. Extensive experiments are conducted to manifest the effectiveness of the proposed approach, and on the basis of Area under Curve values and computed accuracy values, it is concluded that Inception-ResNet-v2 and Xception models outperform other competitive models. The findings show the potential of this technology in the early identification of glaucoma. This automated diagnosis system has great potential to ultimately reduce the human efforts and precious time of ophthalmologists.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sharda University, Greater Noida,, India

    Law Kumar Singh &  Pooja

  2. Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India

    Law Kumar Singh & Munish Khanna

  3. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Hitendra Garg

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  1. Law Kumar Singh
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  2. Pooja
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Singh, L.K., Pooja, Garg, H. et al. Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. Evolving Systems 13, 807–836 (2022). https://doi.org/10.1007/s12530-022-09426-4

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