Automatic Glaucoma Diagnosis in Digital Fundus Images Using Deep CNNs

The World Health Organization (WHO) approximates that more than 42 million people are currently blind in the world, 80 per cent of which could have been prevented or cured by early detection. According to a survey, glaucoma is the second most leading cause for blindness after cataract. It is an irreversible eye disease, and once the vision is lost, it cannot be recovered. Thus, it is vital to develop an automatic computerized tool to diagnose the disease. In this paper, a novel and robust deep learning-based convolutional neural networks (CNNs) architecture has been proposed to deal with the problem. The network consists of six convolutional layers, with various activation functions, and pooling layers to get the abstract and detailed information of the input image. The proposed architecture predicts the probability of an image being glaucoma. The model has been experimented with Refugee and Drishti datasets. Our proposed model is able to diagnose the glaucoma disease automatically with an accuracy of 90%, sensitivity of 96%, and specificity of 84%, respectively.

[1]  Majid A. Al-Taee,et al.  Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis , 2018, Symmetry.

[2]  Jayanthi Sivaswamy,et al.  Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[3]  J. Liu,et al.  Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Tien Yin Wong,et al.  Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening , 2013, IEEE Transactions on Medical Imaging.

[5]  Tien Yin Wong,et al.  Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[6]  A. Sevastopolsky,et al.  Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network , 2017, Pattern Recognition and Image Analysis.

[7]  B. Naveen Kumar,et al.  Detection of Glaucoma using image processing techniques: A review , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[8]  K. Narasimhan,et al.  AN EFFICIENT AUTOMATED SYSTEM FOR GLAUCOMA DETECTION USING FUNDUS IMAGE , 2011 .

[9]  Mohamed Akil,et al.  Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images , 2019, Comput. Medical Imaging Graph..

[10]  Stephen Lin,et al.  Efficient Reconstruction-Based Optic Cup Localization for Glaucoma Screening , 2013, MICCAI.

[11]  M. Sonka,et al.  Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.

[12]  Kaamran Raahemifar,et al.  Optic disc segmentation for glaucoma screening system using fundus images , 2017, Clinical ophthalmology.

[13]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[14]  Tien Yin Wong,et al.  Glaucoma detection based on deep convolutional neural network , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Kaamran Raahemifar,et al.  An Automatic Image Processing System for Glaucoma Screening , 2017, Int. J. Biomed. Imaging.

[16]  Mamta Juneja,et al.  Automated detection of Glaucoma using deep learning convolution network (G-net) , 2019, Multimedia Tools and Applications.

[17]  R. Varma,et al.  The Optic Nerve in Glaucoma , 1993 .

[18]  Qaisar Abbas,et al.  Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning , 2017 .

[19]  Shuicheng Yan,et al.  Automatic Feature Learning for Glaucoma Detection Based on Deep Learning , 2015, MICCAI.

[20]  Thomas G. Dietterich.  Adaptive computation and machine learning , 1998 .

[21]  Kaamran Raahemifar,et al.  Retinal fundus images for glaucoma analysis: the RIGA dataset , 2018, Medical Imaging.

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features , 2011, IEEE Transactions on Information Technology in Biomedicine.

[24]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[25]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[26]  Jayanthi Sivaswamy,et al.  A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis , 2015 .

[27]  Valery Naranjo,et al.  CNNs for automatic glaucoma assessment using fundus images: an extensive validation , 2019, BioMedical Engineering OnLine.