Classification of white blood cells using deep learning

Nowadays, medical image analysis has become an increasingly indispensable tool during the diagnosis and treatment of many illnesses. Particularly, white blood cell images observation matters. The amount trend of different classes of white blood cells can serve as a prediction of many blood diseas...

Full description

Saved in:
Bibliographic Details
Main Author: Zhang, Mengxin
Other Authors: Wen Bihan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158876
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-158876
record_format dspace
spelling sg-ntu-dr.10356-1588762023-07-04T17:49:02Z Classification of white blood cells using deep learning Zhang, Mengxin Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Nowadays, medical image analysis has become an increasingly indispensable tool during the diagnosis and treatment of many illnesses. Particularly, white blood cell images observation matters. The amount trend of different classes of white blood cells can serve as a prediction of many blood diseases. Deep learning has been widely used in the medical image analysis area. However, in the medical imaging area, because of reasons such as privacy and imbalance of patients, rich data sets with accurate annotations are always hard to make. As a result of that, transfer learning sometimes is chosen as a method to make up for this deficiency. The experiment of this dissertation is based on a newly proposed white blood cells data set Raabin-WBC with the objective of classifying five different classes of leukocytes: neutrophils, monocytes, lymphocytes, eosinophils, and basophils. Three kinds of traditional models with great performance in other tasks are used: VGG, ResNet, and ResNext. For each architecture, different network depths are used. Transfer learning is used for models with large depth. The results show that those models perform well in test set A which is similar to the training set while performing badly in test set B which differs a lot from the training set. Besides, several comparative analyses are made to show the effect of different influence factors such as network depth. Master of Science (Signal Processing) 2022-05-31T05:46:17Z 2022-05-31T05:46:17Z 2022 Thesis-Master by Coursework Zhang, M. (2022). Classification of white blood cells using deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158876 https://hdl.handle.net/10356/158876 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Zhang, Mengxin
Classification of white blood cells using deep learning
description Nowadays, medical image analysis has become an increasingly indispensable tool during the diagnosis and treatment of many illnesses. Particularly, white blood cell images observation matters. The amount trend of different classes of white blood cells can serve as a prediction of many blood diseases. Deep learning has been widely used in the medical image analysis area. However, in the medical imaging area, because of reasons such as privacy and imbalance of patients, rich data sets with accurate annotations are always hard to make. As a result of that, transfer learning sometimes is chosen as a method to make up for this deficiency. The experiment of this dissertation is based on a newly proposed white blood cells data set Raabin-WBC with the objective of classifying five different classes of leukocytes: neutrophils, monocytes, lymphocytes, eosinophils, and basophils. Three kinds of traditional models with great performance in other tasks are used: VGG, ResNet, and ResNext. For each architecture, different network depths are used. Transfer learning is used for models with large depth. The results show that those models perform well in test set A which is similar to the training set while performing badly in test set B which differs a lot from the training set. Besides, several comparative analyses are made to show the effect of different influence factors such as network depth.
author2 Wen Bihan
author_facet Wen Bihan
Zhang, Mengxin
format Thesis-Master by Coursework
author Zhang, Mengxin
author_sort Zhang, Mengxin
title Classification of white blood cells using deep learning
title_short Classification of white blood cells using deep learning
title_full Classification of white blood cells using deep learning
title_fullStr Classification of white blood cells using deep learning
title_full_unstemmed Classification of white blood cells using deep learning
title_sort classification of white blood cells using deep learning
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158876
_version_ 1772827168068337664