RetinalNet-500: a newly developed CNN model for eye disease detection

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

ग्रंथसूची विवरण
मुख्य लेखकों:	Toki, Sadikul Alim, Rahman, Sohanoor, Fahim, SM Mohtasim Billah, Mostakim, Abdullah Al
अन्य लेखक:	Rahman, Md. Khalilur
स्वरूप:	थीसिस
भाषा:	English
प्रकाशित:	Brac University 2023
विषय:	Retinal diagnosis Fundus images CNN Deep learning ML Machine learning Cognitive learning theory
ऑनलाइन पहुंच:	http://hdl.handle.net/10361/18039

id	10361-18039
record_format	dspace
spelling	10361-180392023-03-30T21:01:41Z RetinalNet-500: a newly developed CNN model for eye disease detection Toki, Sadikul Alim Rahman, Sohanoor Fahim, SM Mohtasim Billah Mostakim, Abdullah Al Rahman, Md. Khalilur Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Retinal diagnosis Fundus images CNN Deep learning ML Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-31). Fundus images are commonly used by medical experts like ophthalmologists, which are very helpful in detecting various retinal disorders. They used this to diagnose the different types of eye diseases like Cataracts, Diabetic Retinopathy, Glaucoma etc. These fundus images can be also used for the prediction of the severity of the diseases and can provide early signs or warnings. Recently, different machine learning algorithms are playing a vital role in the field of medical science, and it is no different in Ophthalmology either. In this research, we aim to automatically classify healthy and diseased retinal fundus images using deep neural networks. Because deep learning is an excellent machine learning algorithm, which has proven to be very accurate in computer vision problems. In our research, we used convolutional neural networks(CNN) to classify the retinal images whether they are healthy or not. Sadikul Alim Toki Sohanoor Rahman SM Mohtasim Billah Fahim Abdullah Al Mostakim B. Computer Science 2023-03-30T04:47:43Z 2023-03-30T04:47:43Z 2022 2022-05 Thesis ID 18101467 ID 21141072 ID 18101147 ID 19301268 http://hdl.handle.net/10361/18039 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. 31 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Retinal diagnosis Fundus images CNN Deep learning ML Machine learning Cognitive learning theory
spellingShingle	Retinal diagnosis Fundus images CNN Deep learning ML Machine learning Cognitive learning theory Toki, Sadikul Alim Rahman, Sohanoor Fahim, SM Mohtasim Billah Mostakim, Abdullah Al RetinalNet-500: a newly developed CNN model for eye disease detection
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Rahman, Md. Khalilur
author_facet	Rahman, Md. Khalilur Toki, Sadikul Alim Rahman, Sohanoor Fahim, SM Mohtasim Billah Mostakim, Abdullah Al
format	Thesis
author	Toki, Sadikul Alim Rahman, Sohanoor Fahim, SM Mohtasim Billah Mostakim, Abdullah Al
author_sort	Toki, Sadikul Alim
title	RetinalNet-500: a newly developed CNN model for eye disease detection
title_short	RetinalNet-500: a newly developed CNN model for eye disease detection
title_full	RetinalNet-500: a newly developed CNN model for eye disease detection
title_fullStr	RetinalNet-500: a newly developed CNN model for eye disease detection
title_full_unstemmed	RetinalNet-500: a newly developed CNN model for eye disease detection
title_sort	retinalnet-500: a newly developed cnn model for eye disease detection
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/18039
work_keys_str_mv	AT tokisadikulalim retinalnet500anewlydevelopedcnnmodelforeyediseasedetection AT rahmansohanoor retinalnet500anewlydevelopedcnnmodelforeyediseasedetection AT fahimsmmohtasimbillah retinalnet500anewlydevelopedcnnmodelforeyediseasedetection AT mostakimabdullahal retinalnet500anewlydevelopedcnnmodelforeyediseasedetection
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RetinalNet-500: a newly developed CNN model for eye disease detection

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