•   Tian Jipeng

  •   Manasa S.

  •   T. C. Manjunath


Glaucoma is a group of eye diseases that cause damage to the optic nerve, causing the successive narrowing of the visual field in affected patients due to increased intraocular pressure, which can lead the patient, at an advanced stage, to blindness without clinical reversal. As we have heard and seen from generations across that Glaucoma has been and is still one of the leading diseases that has permanent damage if untreated. As per the current research it says that 79 Million are affected BY 2020 which are untreated. So, to make it easy for us humans, early detection is one of the best way to create awareness and treat the diseased. After having gone through the majority of the literatures, have seen that when LBP is given to HOG has accurate results for better feature extraction than other methods, also application of Cuckoo search (CS) algorithm, Random forest (for classifying) and Conventional Neural Network (for segmentation) have better outcome compared to the previously used hybrid algorithm methods to detected the diseased from the normal eye. So, to achieve this I will be using Matlab tool as it produces more accurate results than any other platform. In one of the paper LBP algorithm has been extensively used to obtain the desired results but when learnt about HOG, it looked as it has better properties to enhance the required results when combined along with LBP. CS is another unique method to analyze on aggregation of the image texture.

Keywords: Glaucoma, Matlab, Simulation, Detection.


S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans. on Neural Networks, vol. 4, pp. 570-578, July 1993.

[1]. Babu, TR Ganesh, and S. Shenbagadevi. "Automatic detection of glaucoma using fundus image." European Journal of Scientific Research 59.1 (2011): 22-32.

WHO: Glaucoma bulletin 2011 http://www.who.int/bulletin/volumes/82/11/features1104/en/ , 2011

Liu, J., et al. "Automatic glaucoma diagnosis from fundus image." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011, IEEE

Sujata Sainil and Komal Arora, “A Study Analysis on the Different Image Segmentation Techniques”, RI Publications Int. Jour. of Info. & Computation Tech., ISSN 0974-2239, Vol. 4, No. 14, pp. 1445-1452, 2014.

Balasubramanian T, Krishnan S, Mohanakrishnan M, Ramnarayan Rao K, “HOG Feature based SVM Classification of Glaucomatous Fundus Image with Extraction of Blood Vessels” 978-1-5090-3646-2/16/, 2016 IEEE

Guo, Zhenhua, Lie Zhang & David Zhang “A complete modeling of local binary pattern operator for texture classification” Image Processing, IEEE Transactions on 2010: 1657-1663.

Swathi Ramachandran, Dr. T.C.Manjunath “Higher order glaucoma in humans using Hybrid BMIP algos” International Journal of Management, Technology & Engineering, 2249-7455, 2018

Krit Inthajaki, Cattleya Duanggate, Bunyarit Uyyanonvara, and Stanislav S. Makhanov “Medical Image Blob Detection with Feature Stability and KNN Classification” JCSSE, 978-1-4577-0687-5 2011 IEEE.

C. Angulo, L. Gonz´alez, A. Catal`a, and F. Velasco, “Multi-classification with tri-class support vector machines: A review,” in Proc. 9th Int. Work Conf. Artif. Neural Netw., 2011, pp. 276–283

Mahendran Gandhi and Dr. R. Dhanasekaran, “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifier”, IEEE Int. Conf. on Communication & Signal Proc., Melmaruvathur, TN, India, pp. 873-877, 3-5 April 2013.


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How to Cite
Jipeng, T., S., M. and Manjunath, T. 2020. An Optimized Method Using CNN, RF, Cuckoo Search and HOG for Early Detection of Eye Disease in Humans. European Journal of Electrical Engineering and Computer Science. 4, 2 (Apr. 2020). DOI:https://doi.org/10.24018/ejece.2020.4.2.202.