An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification

Author:

Aamir MuhammadORCID,Irfan MuhammadORCID,Ali TariqORCID,Ali GhulamORCID,Shaf AhmadORCID,S Alqahtani SaeedORCID,Al-Beshri Ali,Alasbali Tariq,Mahnashi Mater H.

Abstract

Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases.

Publisher

MDPI AG

Subject

Clinical Biochemistry

Cited by 32 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Recognition of eye diseases based on deep neural networks for transfer learning and improved D-S evidence theory;BMC Medical Imaging;2024-01-18

2. Deep convolutional neural network for glaucoma detection based on image classification;Journal of Intelligent & Fuzzy Systems;2024-01-10

3. Artificial intelligence in glaucoma: opportunities, challenges, and future directions;BioMedical Engineering OnLine;2023-12-16

4. The Proposed Convolutional Neural Network Architecture for the Detection and Classification of Eye Diseases;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

5. New Deep Learning Models for Medical Imaging: Deep Belief Network, GAN, Autoencoder;2023 4th International Conference on Smart Electronics and Communication (ICOSEC);2023-09-20

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