Automated glaucoma diagnosis using deep learning approach

Al-Bander, Baidaa; Al-Nuaimy, Waleed; Al-Taee, Majid A.; Zheng, Yalin

doi:10.1109/ssd.2017.8166974

Cited by 74 publications

(38 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [40] the authors applied a six layers architecture to optic disc patches previously segmented. In [41] the authors used CNN to extract features and train a SVM classifier to detect glaucoma. Recently Fu et.…”

Section: Fig 1 Of the With G Optic Inferio Tempomentioning

confidence: 99%

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Gómez-Valverde

Antón

Fatti

et al. 2019

Biomed. Opt. Express

184

120

View full text Add to dashboard Cite

Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.

show abstract

Section: Fig 1 Of the With G Optic Inferio Tempomentioning

confidence: 99%

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Gómez-Valverde

Antón

Fatti

et al. 2019

Biomed. Opt. Express

184

120

View full text Add to dashboard Cite

show abstract

“…On they publicly available ARIA dataset, the authors achieved an Acc of 96.73%, specificity of 97.89% and sensitivity of 79.32%, respectively. Al-Bander [74] used CNN for feature extraction and SVM for Gl and non Gl classification. They achieved an Acc of 88.2%, specificity of 90.8% and sensitivity of 85%, respectively.…”

Section: Approaches Employing Combined DL and Mlmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

View full text Add to dashboard Cite

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

show abstract

“…Anyhow, this method was not advisable for earlier stage detection of glaucoma. Al‐Bander et al 31 developed an automated approach based on CNN, which diagnosing glaucoma with considerably lower computational cost, but it was only applicable for the smaller dataset. Gómez‐Valverde et al 32 developed glaucoma detection based on the CNN approach, which was applicable for the large scale datasets, but the classification carried out only on color fundus images.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

Ajesh¹,

Ravi²

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization‐driven classifier is developed for glaucoma detection. The proposed Jaya‐chicken swarm optimization (Jaya‐CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya‐CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya‐CSO. The proposed Jaya‐CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.

show abstract

Automated glaucoma diagnosis using deep learning approach

Cited by 74 publications

References 14 publications

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

Contact Info

Product

Resources

About