Early detection of eye disease in humans using Random Forest & HOG concepts

International Journal of Electronics and Communication Engineering

© 2020 by SSRG - IJECE Journal

Volume 7 Issue 4

Year of Publication : 2020

Authors : Tian Jipeng, Manasa S., Dr. T.C.Manjunath

10.14445/23488549/IJECE-V7I4P102

How to Cite?

Tian Jipeng, Manasa S., Dr. T.C.Manjunath, "Early detection of eye disease in humans using Random Forest & HOG concepts," SSRG International Journal of Electronics and Communication Engineering, vol. 7,  no. 4, pp. 5-7, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I4P102

Abstract:

In this paper, a brief introduction to early detection of eye disease in humans using Random Forest & HOG concepts. The result is the outcome of the Post-Graduate project work of the student that will be carried out in the second year of the course & this work is just the synopsis that is being framed for the carrying out of the detection of glaucoma disease. A 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, glaucoma has been and is still one of the leading diseases that permanently damage if untreated. As per the current research, it says that 79Million are affected BY 2020, which are untreated. To make it easy for us humans, early detection is one of the best ways to create awareness and treat the diseased. After having gone through the majority of the literature, 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 detect the diseased from the normal eye. So, to achieve this, I will be using the Matlab tool to produce more accurate results than any other platform. In one of the papers, the LBP algorithm has been extensively used to obtain the desired results, but when learned about HOG, it looked as if it has better properties to enhance the required results when combined with LBP. CS is another unique method to analyze an aggregation of the image texture.

Keywords:

Glaucoma, HOG, Random Forest, Matlab, Simulation

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