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103259

Classification of Corneal Pattern Based on Convolutional Neural Network

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Last updated: 25 Dec 2024

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Abstract

The early discovery of the disease is a great achievement in management of the cornea. This paper presents an efficient approach for the classification of normal and abnormal corneal patterns based on deep learning. Convolutional Neural Networks (CNNs) are utilized for this purpose. The CNN model built for this purpose comprises5 layers. The classification process is achieved through two stages. Automatic feature extraction based CNN is applied in the first stage, followed by sequence of processing layers includes: pooling layer, dropout layer and fully connected layer resulted in a diagnosis of the condition of the patient in terms of normal or abnormal. The proposed technique was tested and evaluated based MATLAB environment on a set of corneal images. These images were collected for patients based on confocal microscopy. The CNN classification results on corneal fundus images recorded an accuracy of 100 %.

DOI

10.21608/mjeer.2020.103259

Keywords

Deep learning, Convolutional neural network (CNN), Normal and abnormal corneal images and model accuracy

Authors

First Name

Nehad T.

Last Name

Haggag

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-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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First Name

Ahmed

Last Name

Sedik

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Affiliation

Dep. Of electrical engineering, Faculty of electronic Engineering, Menia University, Egypt.

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First Name

Ghada M.

Last Name

Elbanby

MiddleName

-

Affiliation

Dep. of Ind. Electronics and Control Eng., Faculty of Electronic Engineering,Menoufia University, Egypt.

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First Name

Adel S.

Last Name

El-Fishawy

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-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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Orcid

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First Name

Waleed

Last Name

El-Shafai

MiddleName

-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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First Name

Ashraf

Last Name

Khalaf

MiddleName

-

Affiliation

Dep. Of electrical engineering, Faculty of electronic Engineering, Menia University, Egypt.

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First Name

El-Sayed M.

Last Name

El-Rabie

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-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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First Name

Moawad I-

Last Name

Dessouky

MiddleName

-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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Orcid

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First Name

Nabil A.

Last Name

Ismail

MiddleName

-

Affiliation

Dep. Of Computers Engineering. Faculty of Electronic Engineering, Menoufia University, Egypt.

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Orcid

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First Name

Fathi E.

Last Name

Abd El-Samie

MiddleName

-

Affiliation

Dept. of Communications Faculty of Electronic Engineeing, Menofia University Menouf, Egypt

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Volume

29

Article Issue

2

Related Issue

15327

Issue Date

2020-07-01

Receive Date

2020-07-16

Publish Date

2020-07-01

Page Start

9

Page End

14

Print ISSN

1687-1189

Online ISSN

2682-3535

Article File

MJEER_Volume 29_Issue 2_Pages 9-14.pdf

PDF . 1.0MB

Link

https://mjeer.journals.ekb.eg/article_103259.html

Detail API

https://mjeer.journals.ekb.eg/service?article_code=103259

Order

2

Type

Original Article

Type Code

1,088

Publication Type

Journal

Publication Title

Menoufia Journal of Electronic Engineering Research

Publication Link

https://mjeer.journals.ekb.eg/

MainTitle

Classification of Corneal Pattern Based on Convolutional Neural Network

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Type

Article

Created At

22 Jan 2023