An efficient deep learning approach to classify white blood cells

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

Bibliografske podrobnosti
Main Authors:	Turja, Afif Ibna Kadir Khan, Habib, Ahsan, Ehsani, Kefaiat Lamia, Shabab, Zahin, Tamanna, Anika Nower
Drugi avtorji:	Zaman, Shakila
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2024
Teme:	Deep learning Machine learning Convolutional neural network Data mining Machine learning > Industrial applications Neural networks (Computer science)
Online dostop:	http://hdl.handle.net/10361/22818

id	10361-22818
record_format	dspace
spelling	10361-228182024-07-14T10:34:39Z An efficient deep learning approach to classify white blood cells Turja, Afif Ibna Kadir Khan Habib, Ahsan Ehsani, Kefaiat Lamia Shabab, Zahin Tamanna, Anika Nower Zaman, Shakila Department of Computer Science and Engineering, Brac University Deep learning Machine learning Convolutional neural network Data mining Machine learning--Industrial applications 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 26-28). The human immune system’s white blood cells (WBCs) fight against infection and prevent the body from potentially harmful substances. They consist of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, each of which comprises a various amount and has a specific task to do. Identifying white blood cells has been one of the most critical parts in medical science because it helps in diagnosing and monitoring various diseases and disorders. The manual microscopic analysis is difficult and subjective, and its time-consuming nature reduces the statistical dependability of the results. Problem with existing deep learning methods is that they can be heavy on computation. In this paper, we have proposed a very lightweight and efficient methodology which is called L100K-NetV2 with only 97,704 trainable parameters to classify white blood cells. The experiment, done with the Raabin- WBC (R-WBC) dataset, managed to achieve an accuracy of 98.11% in the TestA set. The proposed deep-learning methodology outperformed many other pre-trained deep learning models in terms of accuracy and parameter counts which helps to decrease the computational cost and training time. Afif Ibna Kadir Khan Turja Ahsan Habib Kefaiat Lamia Ehsani Zahin Shabab Anika Nower Tamanna B.Sc. in Computer Science and Engineering 2024-05-14T07:58:05Z 2024-05-14T07:58:05Z ©2023 2023-09 Thesis ID 18101407 ID 22241135 ID 17201097 ID 20101165 ID 23241044 http://hdl.handle.net/10361/22818 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. 35 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Machine learning Convolutional neural network Data mining Machine learning--Industrial applications Neural networks (Computer science)
spellingShingle	Deep learning Machine learning Convolutional neural network Data mining Machine learning--Industrial applications Neural networks (Computer science) Turja, Afif Ibna Kadir Khan Habib, Ahsan Ehsani, Kefaiat Lamia Shabab, Zahin Tamanna, Anika Nower An efficient deep learning approach to classify white blood cells
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	Zaman, Shakila
author_facet	Zaman, Shakila Turja, Afif Ibna Kadir Khan Habib, Ahsan Ehsani, Kefaiat Lamia Shabab, Zahin Tamanna, Anika Nower
format	Thesis
author	Turja, Afif Ibna Kadir Khan Habib, Ahsan Ehsani, Kefaiat Lamia Shabab, Zahin Tamanna, Anika Nower
author_sort	Turja, Afif Ibna Kadir Khan
title	An efficient deep learning approach to classify white blood cells
title_short	An efficient deep learning approach to classify white blood cells
title_full	An efficient deep learning approach to classify white blood cells
title_fullStr	An efficient deep learning approach to classify white blood cells
title_full_unstemmed	An efficient deep learning approach to classify white blood cells
title_sort	efficient deep learning approach to classify white blood cells
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22818
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_version_	1814309233788715008

An efficient deep learning approach to classify white blood cells

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