CNN model Channel Separation for glaucoma Color Spectral Detection


  • Murugan Subbiah Product Design Specialist, Embedded System, Vee Eee Technologies Chennai
  • S. Mohan Kumar Dean & Professor, Department of CSE, Nagarjuna College of Engineering and Technology, Bangalore, India
  • T.R.Ganesh Babu,ME,Ph.D, Professor, Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram-637408, Namakkal district, Tamil Nadu, India.


Glaucoma is a leading type of eye disease that affects the optic nerve causing permanent vision loss. As this optic nerve is important for good vision safety, glaucoma can be prevented only if detected earlier. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis with abnormally high eye pressure. A descriptive analysis of glaucoma diagnosis using Convolutional Neural Network (CNN) for spectral color detection of the eye is presented in this study. Initially, the color components are separated as red, green, and blue. Then, the region of Interest (ROI) is obtained from the green channel of the fundus image for the feature extraction. The Green channel is used for the fundus image analysis and detection because it is the only sensitive and high contrast color that the other two components for humans. Finally, the proposed system's glaucoma color spectral diagnosis and its performance are analyzed using CNN with accuracy.


Download data is not yet available.


Xiong, L., Li, H., & Zheng, Y. (2014, June). Automatic detection of glaucoma in retinal images. In 2014 9th IEEE Conference on Industrial Electronics and Applications (pp. 1016-1019). IEEE.

Thangaraj, V., & Natarajan, V. (2017, June). Glaucoma diagnosis using support vector machine. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 394-399). IEEE.

Cheng, J., Liu, J., Wong, D. W. K., Tan, N. M., Lee, B. H., Cheung, C., ... & Aung, T. (2011, August). Focal edge association to glaucoma diagnosis. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4481-4484). IEEE.

Fink, F., Worle, K., Gruber, P., Tome, A. M., Gorriz-Saez, J. M., Puntonet, C. G., & Lang, E. W. (2008, August). ICA analysis of retina images for glaucoma classification. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4664-4667). IEEE.

Yadav, D., Sarathi, M. P., & Dutta, M. K. (2014, August). Classification of glaucoma based on texture features using neural networks. In 2014 Seventh International Conference on Contemporary Computing (IC3) (pp. 109-112). IEEE.

Simonthomas, S., Thulasi, N., & Asharaf, P. (2014, February). Automated diagnosis of glaucoma using Haralick texture features. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-6). IEEE.

Pathan, S., Kumar, P., & Pai, R. M. (2018, September). The role of color and texture features in glaucoma detection. In 2018 International Conference on Advances in Computing, Communications, and Informatics (ICACCI) (pp. 526-530). IEEE.

Kavya, N., & Padmaja, K. V. (2017, October). Glaucoma detection using texture features extraction. In 2017 51st Asilomar Conference on Signals, Systems, and Computers (pp. 1471-1475). IEEE.

Maharaja, D., & Shaby, M. (2017). Empirical Wavelet Transform and GLCM Feature Based Glaucoma Classification from Fundus Image. International Journal of MC Square Scientific Research, 9(1), 78-85.

Manahoran, N., & Srinath, M. V. (2017). K-Means Clustering Based Marine Image Segmentation. International Journal of MC Square Scientific Research, 9(3), 26-29.

Meysam Tavakoli, Reza Pourreza Shahri, Hamidreza Pourreza, Alireza Mehdizadeh, Touka Banaee, Mohammad Hosein Bahreini Toosi, (2013), ‘A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy, Pattern Recognition, Vol. 46, No. 10, 2013, pp. 2740-2753.

Marwan D. Saleh, C. Eswaran, (2012), 'An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and Has Detection,' Computer Methods and Programs in Biomedicine, Vol. 108, No. 1, pp. 186–196.

Fangyan Nie, Yonglin Wang, Meisen Pan, Guanghan Peng, Pingfeng Zhang, (2013), 'Two-dimensional extension of variance-based thresholding for image segmentation, Multidim Syst Sign Process, Vol. 24, No. 3, pp. 485–501.

M. Usman Akram, Shehzad Khalid, Shoab A.Khan, (2013), ‘Identification and classification of microaneurysms for early detection of diabetic retinopathy, Pattern Recognition, Vol. 46, No. 1, pp. 107–116.

Kumar, B. N., Chauhan, R. P., & Dahiya, N. (2016). Detection of Glaucoma using image processing techniques: A review. Microelectronics, Computing, and Communications (MicroCom), 2016 International Conference on (pp. 1-6). IEEE.