Comparative analysis of neural network Models for peripheral blood cell image classification

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

书目详细资料
Main Authors:	Mollah, Md Tanzid, Shahriar, Mahi, Fahim, Mohammad, Ahmed, Zehan, Sakib, Kaji Sadman
其他作者:	Hossain, Muhammad Iqbal
格式:	Thesis
语言:	English
出版:	Brac University 2024
主题:	Medicine Diagnosis Leukemia Efficient- Net Neural networks (Computer science)
在线阅读:	http://hdl.handle.net/10361/23458

id	10361-23458
record_format	dspace
spelling	10361-234582024-06-13T21:01:10Z Comparative analysis of neural network Models for peripheral blood cell image classification Mollah, Md Tanzid Shahriar, Mahi Fahim, Mohammad Ahmed, Zehan Sakib, Kaji Sadman Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Medicine Diagnosis Leukemia Efficient- Net Neural networks (Computer science) 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 36-38). It’s quite difficult to fathom that the future of medicine and diagnosis is more dependent on, and much more likely to be dictated by the growth of technology than the quality of doctors. One of the deadliest diseases today that is ubiquitous all around the world is Leukemia. As deadly as it is, it is one of the most difficult diseases to diagnose. One of the biggest challenges is to identify cells as being affected by this condition and this requires highly trained medical professionals to accomplish such tasks. In this paper we have trained four different image processing models to recognize and identify such cancerous cells. We have used more than 9000 images to do so. After the training processes were over, we evaluated the success of these individual models to assess the difference in their final accuracies, we should bear in mind that these images are rather different than what a usual image-based dataset would look like in that the images are quite similar despite being of different classes. We have used the following models: YOLOv5 (precision = 0.82), CNN (precision = 0.74), YOLOv7 (precision = 0.52), EfficientNet (accuracy = 0.89). From this we can clearly agree upon the dominance of EfficientNet over all the other models. Md Tanzid Mollah Mahi Shahriar Mohammad Fahim Kaji Sadman Sakib Zehan Ahmed B.Sc in Computer Science 2024-06-13T11:54:41Z 2024-06-13T11:54:41Z ©2023 2023-09 Thesis ID 22141053 ID 19301252 ID 19301041 ID 19301243 ID 19301059 http://hdl.handle.net/10361/23458 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. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Medicine Diagnosis Leukemia Efficient- Net Neural networks (Computer science)
spellingShingle	Medicine Diagnosis Leukemia Efficient- Net Neural networks (Computer science) Mollah, Md Tanzid Shahriar, Mahi Fahim, Mohammad Ahmed, Zehan Sakib, Kaji Sadman Comparative analysis of neural network Models for peripheral blood cell image classification
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	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Mollah, Md Tanzid Shahriar, Mahi Fahim, Mohammad Ahmed, Zehan Sakib, Kaji Sadman
format	Thesis
author	Mollah, Md Tanzid Shahriar, Mahi Fahim, Mohammad Ahmed, Zehan Sakib, Kaji Sadman
author_sort	Mollah, Md Tanzid
title	Comparative analysis of neural network Models for peripheral blood cell image classification
title_short	Comparative analysis of neural network Models for peripheral blood cell image classification
title_full	Comparative analysis of neural network Models for peripheral blood cell image classification
title_fullStr	Comparative analysis of neural network Models for peripheral blood cell image classification
title_full_unstemmed	Comparative analysis of neural network Models for peripheral blood cell image classification
title_sort	comparative analysis of neural network models for peripheral blood cell image classification
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23458
work_keys_str_mv	AT mollahmdtanzid comparativeanalysisofneuralnetworkmodelsforperipheralbloodcellimageclassification AT shahriarmahi comparativeanalysisofneuralnetworkmodelsforperipheralbloodcellimageclassification AT fahimmohammad comparativeanalysisofneuralnetworkmodelsforperipheralbloodcellimageclassification AT ahmedzehan comparativeanalysisofneuralnetworkmodelsforperipheralbloodcellimageclassification AT sakibkajisadman comparativeanalysisofneuralnetworkmodelsforperipheralbloodcellimageclassification
_version_	1814307157134278656

Comparative analysis of neural network Models for peripheral blood cell image classification

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