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phat 24(1):

Research Article

Glaucoma Detection Using Explainable AI and Deep Learning

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  • @ARTICLE{10.4108/eetpht.10.5658,
        author={Najeeba Afreen and Rajanikanth Aluvalu},
        title={Glaucoma Detection Using Explainable AI and Deep Learning},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={4},
        keywords={ANFIS \& SNN Fuzzy layer, VGG19, AlexNet, ResNet, MobileNet, Fundus Image},
        doi={10.4108/eetpht.10.5658}
    }
    
  • Najeeba Afreen
    Rajanikanth Aluvalu
    Year: 2024
    Glaucoma Detection Using Explainable AI and Deep Learning
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5658
Najeeba Afreen1,*, Rajanikanth Aluvalu1
  • 1: Chaitanya Bharathi Institute of Technology
*Contact email: afreennajeefa@gmail.com

Abstract

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. OBJECTIVES:  conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.  RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

Keywords
ANFIS & SNN Fuzzy layer, VGG19, AlexNet, ResNet, MobileNet, Fundus Image
Received
2023-12-29
Accepted
2024-03-28
Published
2024-04-05
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5658

Copyright © 2024 N. Afreen et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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