Enhancing eye disease classification through synergistic deep learning approaches

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Bibliographic Details
Main Authors:	Rahaman, Asif, Mahamud, Shifat, Akter, Shanjida, Saha, Dipro, Fahad
Other Authors:	Rahman, Rafeed
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	Hybrid structure Resnet50 VGG19 Multi-class classification Eye > Diseases Data mining
Online Access:	http://hdl.handle.net/10361/23550

id	10361-23550
record_format	dspace
spelling	10361-235502024-06-24T21:03:19Z Enhancing eye disease classification through synergistic deep learning approaches Rahaman, Asif Mahamud, Shifat Akter, Shanjida Saha, Dipro Fahad Rahman, Rafeed Dofadar, Dibyo Fabian Department of Computer Science and Engineering, Brac University Hybrid structure Resnet50 VGG19 Multi-class classification Eye--Diseases Data mining This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-38). The number of people living with blindness is about 43 million people and 295 million people are living with moderate-to-severe visual impairment. The leading causes of most blindness are macular degeneration, diabetic retinopathy, and glaucoma. Moreover, the early stages of most eye diseases are asymptomatic. As a result, determining the cause becomes very difficult, and if left untreated, there can be irreversible damage to vision. This paper discusses a hybrid structure that combined ResNet50 and VGG19 to successfully classify and predict various eye diseases accurately. In addition, we used transfer learning and multi-class classification, which gave us an accuracy of 94.7%, whereas previous approaches with traditional CNN only gave an accuracy of less than 85%. This study has the potential to significantly contribute to the timely identification and precise categorization of ocular disorders, hence leading to advancements in patient treatment, increased overall well-being, and a more promising outlook for individuals affected by visual disabilities. Moreover, it indicates the possibility of wider utilization of sophisticated deep learning methods in the field of medical image analysis. Asif Rahaman Shifat Mahamud Shanjida Akter Dipro Saha Fahad B.Sc in Computer Science 2024-06-24T10:14:27Z 2024-06-24T10:14:27Z ©2023 2023-09 Thesis ID 19101605 ID 19101621 ID 20101627 ID 19101614 ID 19101486 http://hdl.handle.net/10361/23550 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Hybrid structure Resnet50 VGG19 Multi-class classification Eye--Diseases Data mining
spellingShingle	Hybrid structure Resnet50 VGG19 Multi-class classification Eye--Diseases Data mining Rahaman, Asif Mahamud, Shifat Akter, Shanjida Saha, Dipro Fahad Enhancing eye disease classification through synergistic deep learning approaches
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Rahman, Rafeed
author_facet	Rahman, Rafeed Rahaman, Asif Mahamud, Shifat Akter, Shanjida Saha, Dipro Fahad
format	Thesis
author	Rahaman, Asif Mahamud, Shifat Akter, Shanjida Saha, Dipro Fahad
author_sort	Rahaman, Asif
title	Enhancing eye disease classification through synergistic deep learning approaches
title_short	Enhancing eye disease classification through synergistic deep learning approaches
title_full	Enhancing eye disease classification through synergistic deep learning approaches
title_fullStr	Enhancing eye disease classification through synergistic deep learning approaches
title_full_unstemmed	Enhancing eye disease classification through synergistic deep learning approaches
title_sort	enhancing eye disease classification through synergistic deep learning approaches
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23550
work_keys_str_mv	AT rahamanasif enhancingeyediseaseclassificationthroughsynergisticdeeplearningapproaches AT mahamudshifat enhancingeyediseaseclassificationthroughsynergisticdeeplearningapproaches AT aktershanjida enhancingeyediseaseclassificationthroughsynergisticdeeplearningapproaches AT sahadipro enhancingeyediseaseclassificationthroughsynergisticdeeplearningapproaches AT fahad enhancingeyediseaseclassificationthroughsynergisticdeeplearningapproaches
_version_	1814308577167278080

Enhancing eye disease classification through synergistic deep learning approaches

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