An Optimized Method Using CNN, RF, Cuckoo Search and HOG for Early Detection of Eye Disease in Humans

Tian Jipeng, Manasa S. and T. C. Manjunath
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Tian Jipeng: Zhongyuan University of Technology, China.
Manasa S.: Dayananda Sagar College of Engineering, India.
T. C. Manjunath: Dayananda Sagar College of Engineering, India.

European Journal of Electrical Engineering and Computer Science, 2020, vol. 4, issue 2

Abstract: 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. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:4:y:2020:i:2:id:19202

DOI: 10.24018/ejece.2020.4.2.202

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