Multiclass Classification and Identification of the External Eye Diseases using Deep CNN

Faizur Rashid; Jamal Abate; Afendi Abdi

doi:10.17485/IJST/v17i12.21

Article

Multiclass Classification and Identification of the External Eye Diseases using Deep CNN

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i12.21

Year: 2024, Volume: 17, Issue: 12, Pages: 1107-1116

Original Article

Multiclass Classification and Identification of the External Eye Diseases using Deep CNN

Faizur Rashid^1*, Jamal Abate², Afendi Abdi³

¹Department of Computer Science, Haramaya University, Ethiopia
²Department of Information Science, Haramaya University, Ethiopia
³Department of Software Engineering, Haramaya University, Ethiopia

*Corresponding Author
Email: [email protected]|

Received Date:03 January 2024, Accepted Date:11 February 2024, Published Date:14 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: These eye illnesses can be either internal eye diseases or external eye diseases. The purpose of research is to find the right model for better performance to identify the external eye disease. The model is customised with 16- layers CNN using multiclass classification. Method: The Deep CNN techniques are utilized with multiclass classification, and the model is developed using Vgg16 with different dropout rates of 0.25 and 0.50 to improve accuracy and performance. In this work, a deep convolutional neural network model is proposed to classify and identify external eye diseases like conjunctivitis, blepharitis, and cellulitis. Datasets were taken in 80:20 randomly from blepharitis, cellulitis, and Conjunctivitis to test (242) and train (968) the model after pre-processing. Novelty: The model is novel and unique using deep CNN, Vgg16, and multiclass classification because it has never been classified and predicted previously for external eye disease. Additionally, Vgg16 with dropout rates of 0.25 and 0.50 was not tested. The model is penetrated into fully connected (FC) layers with different dropout rates. Findings: The accuracy of 98.48% and 0.976% for deep CNN and multiclass classification consecutively produced satisfactory results. The efficiency of R2 is evaluated with multiple classes of data that resulted in a range of 0.425 - 0.775 with k = 10 folds. Vgg16 attains the highest performance of 71.54% with changed dropout rates. The effects of fundus in the ocular, such as retinopathy and AMD, can be examined in the future with segmented data using CNN for better optimization. On account of biological changes in eye and retinal structure, models might be constructed or studied.

Keywords: Multiclass Classification, Identification, Deep CNN, External Eye Disease, Evaluation

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Copyright

© 2024 Rashid et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)