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...
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Format: | Thesis-Master by Coursework |
Language: | English |
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Nanyang Technological University
2022
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Online Access: | https://hdl.handle.net/10356/158876 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
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