GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture

Manassakorn, Anita; Auethavekiat, Supatana; Sa-ing, Vera; Chansangpetch, Sunee; Ratanawongphaibul, Kitiya; Uramphorn, Nopphawan; Tantisevi, Visanee

doi:10.1109/access.2022.3204029

Cited by 15 publications

(2 citation statements)

References 48 publications

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“…Anyone with glaucoma, AMD, or diabetic retinopathy may find the results of this poll very interesting. Anita Manassakorn, Supatana Auethavekiat [19] have assessed CNN's capability to identify glaucoma in OCTA images. They created GlauNet, a specially designed CNN model.…”

Section: Related Studymentioning

confidence: 99%

Glaucoma Detection Using Explainable AI and Deep Learning

Afreen,

Aluvalu

2024

EAI Endorsed Trans Perv Health Tech

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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.

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Section: Related Studymentioning

confidence: 99%

Glaucoma Detection Using Explainable AI and Deep Learning

Afreen,

Aluvalu

2024

EAI Endorsed Trans Perv Health Tech

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“…The output of this process is a sub-image, which is then referred to as an ROI disc. The ROI detection carried out refers to research [4] whose process stages are shown in Figure 2.…”

Section: Pre Processing 1 Region Of Interest (Roi) Disc Detectionmentioning

confidence: 99%

Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning

2022

J. Soft Comput. Explor.

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Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and nonglaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.

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