Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Dettagli Bibliografici
Autori principali:	Nabil, Sheikh MD. Nafis Noor, Ahmed, Sabir, Chowdhury, Naimul Haque, Maria, Farhana Eyesmeen
Altri autori:	Hossain, Muhammad Iqbal
Natura:	Tesi
Lingua:	English
Pubblicazione:	Brac University 2024
Soggetti:	Meta-learning Deep learning Retinal fundus image Prototypical network Retinal disease Image processing Optical data processing Image processing > Digital techniques Machine learning > Medical applications
Accesso online:	http://hdl.handle.net/10361/22857

id	10361-22857
record_format	dspace
spelling	10361-228572024-05-19T21:02:03Z Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning Nabil, Sheikh MD. Nafis Noor Ahmed, Sabir Chowdhury, Naimul Haque Maria, Farhana Eyesmeen Hossain, Muhammad Iqbal Rahman, Rafeed Department of Computer Science and Engineering, Brac University Meta-learning Deep learning Retinal fundus image Prototypical network Retinal disease Image processing Optical data processing Image processing -- Digital techniques Machine learning--Medical applications This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-45). Classifying retinal diseases with a higher accuracy rate is one of the most important means in the medical field. In the case of image classification, finding a dataset becomes a significant challenge for such cases. As a result, the accuracy rate of classification keeps deteriorating. To address this issue of data scarcity and improve the accuracy rate, the Few-Shot method has been proposed. The few-shot learning algorithms integrated into upgraded image classification techniques have been used to enhance retinal images. VGG19 and ResNet50 have been used for feature extraction and VGG19 has given promising results comparatively. Nonetheless, a variation of training episodes was evaluated to acquire the optimal outcome. The proposed method was tested on 4 new classes that are completely different from the training classes and 82% test accuracy was obtained. This acquired result leaves a further scope for potential applications of Few-Shot learning techniques in this medical field. Sheikh MD. Nafis Noor Nabil Sabir Ahmed Naimul Haque Chowdhury Farhana Eyesmeen Maria B.Sc in Computer Science 2024-05-19T03:18:32Z 2024-05-19T03:18:32Z ©2024 2024-01 Thesis ID: 23341121 ID: 20301189 ID: 23341124 ID: 23341127 http://hdl.handle.net/10361/22857 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. 55 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Meta-learning Deep learning Retinal fundus image Prototypical network Retinal disease Image processing Optical data processing Image processing -- Digital techniques Machine learning--Medical applications
spellingShingle	Meta-learning Deep learning Retinal fundus image Prototypical network Retinal disease Image processing Optical data processing Image processing -- Digital techniques Machine learning--Medical applications Nabil, Sheikh MD. Nafis Noor Ahmed, Sabir Chowdhury, Naimul Haque Maria, Farhana Eyesmeen Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Nabil, Sheikh MD. Nafis Noor Ahmed, Sabir Chowdhury, Naimul Haque Maria, Farhana Eyesmeen
format	Thesis
author	Nabil, Sheikh MD. Nafis Noor Ahmed, Sabir Chowdhury, Naimul Haque Maria, Farhana Eyesmeen
author_sort	Nabil, Sheikh MD. Nafis Noor
title	Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
title_short	Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
title_full	Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
title_fullStr	Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
title_full_unstemmed	Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
title_sort	protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22857
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Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning

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