Faster convolutional neural network inferencing for screening red blood cell disease

The peripheral blood smear is a blood test, in which its purpose is to detect or confirm diseases in patients by identifying the morphological characteristic of the blood. The blood smear could only be interpreted through microscope by a skilled laboratory physician or haematologist which is time co...

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Main Author:	Nor Azman, Muhammad Nor Azzafri
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99495/1/MuhammadNorazzafriNorazmanMKE2021.pdf
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id	my-utm-ep.99495
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spelling	my-utm-ep.994952023-02-27T07:50:31Z Faster convolutional neural network inferencing for screening red blood cell disease 2021 Nor Azman, Muhammad Nor Azzafri TK Electrical engineering. Electronics Nuclear engineering The peripheral blood smear is a blood test, in which its purpose is to detect or confirm diseases in patients by identifying the morphological characteristic of the blood. The blood smear could only be interpreted through microscope by a skilled laboratory physician or haematologist which is time consuming and would result in other complication such as higher labour cost and error rate. In Malaysia particularly, the blood smear analysis is only performed in certain general hospital, which have a hematopathology unit, thus having more test to perform at once and ultimately further increases time. Hence, the approach taken to address this issue is by creating an automated blood smear screening tool. Typically feature extraction was done manually in computer vision to identify the disease but this method might introduce a drop in accuracy if the design was under or over segmented. Therefore, this research proposes a convolutional neural network (CNN) to screen diseases from the blood smear images. The focus would be on developing an optimized CNN model in terms of inferencing speed for faster object detection of sickle cell disease. Adopting a popular object detection CNN algorithm i.e., YOLOv3 which has a high inferencing speed, this research performed transfer learning, modify certain CNN attributes by adding a residual block and applying Gaussian filter prior to model training on blood smear images to optimize speed and accuracy. The proposed model, which is to train YOLOv3 with Gaussian filtered dataset achieved an inferencing speed of 379ms per image with a mean average precision of 72.74%. The algorithm had a fast-inferencing speed but suffers in term of accuracy due to insufficient dataset and failing to detect overlapping cells. To solve this, data augmentation technique and sharpening edge of the cell should be performed. In short, this project contributed towards existing literature by developing CNN that prioritize inferencing speed and implement YOLOv3 object detection for localization and classification of sickle cell disease. 2021 Thesis http://eprints.utm.my/id/eprint/99495/ http://eprints.utm.my/id/eprint/99495/1/MuhammadNorazzafriNorazmanMKE2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149810 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Nor Azman, Muhammad Nor Azzafri Faster convolutional neural network inferencing for screening red blood cell disease
description	The peripheral blood smear is a blood test, in which its purpose is to detect or confirm diseases in patients by identifying the morphological characteristic of the blood. The blood smear could only be interpreted through microscope by a skilled laboratory physician or haematologist which is time consuming and would result in other complication such as higher labour cost and error rate. In Malaysia particularly, the blood smear analysis is only performed in certain general hospital, which have a hematopathology unit, thus having more test to perform at once and ultimately further increases time. Hence, the approach taken to address this issue is by creating an automated blood smear screening tool. Typically feature extraction was done manually in computer vision to identify the disease but this method might introduce a drop in accuracy if the design was under or over segmented. Therefore, this research proposes a convolutional neural network (CNN) to screen diseases from the blood smear images. The focus would be on developing an optimized CNN model in terms of inferencing speed for faster object detection of sickle cell disease. Adopting a popular object detection CNN algorithm i.e., YOLOv3 which has a high inferencing speed, this research performed transfer learning, modify certain CNN attributes by adding a residual block and applying Gaussian filter prior to model training on blood smear images to optimize speed and accuracy. The proposed model, which is to train YOLOv3 with Gaussian filtered dataset achieved an inferencing speed of 379ms per image with a mean average precision of 72.74%. The algorithm had a fast-inferencing speed but suffers in term of accuracy due to insufficient dataset and failing to detect overlapping cells. To solve this, data augmentation technique and sharpening edge of the cell should be performed. In short, this project contributed towards existing literature by developing CNN that prioritize inferencing speed and implement YOLOv3 object detection for localization and classification of sickle cell disease.
format	Thesis
qualification_level	Master's degree
author	Nor Azman, Muhammad Nor Azzafri
author_facet	Nor Azman, Muhammad Nor Azzafri
author_sort	Nor Azman, Muhammad Nor Azzafri
title	Faster convolutional neural network inferencing for screening red blood cell disease
title_short	Faster convolutional neural network inferencing for screening red blood cell disease
title_full	Faster convolutional neural network inferencing for screening red blood cell disease
title_fullStr	Faster convolutional neural network inferencing for screening red blood cell disease
title_full_unstemmed	Faster convolutional neural network inferencing for screening red blood cell disease
title_sort	faster convolutional neural network inferencing for screening red blood cell disease
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/99495/1/MuhammadNorazzafriNorazmanMKE2021.pdf
_version_	1776100604827926528

Faster convolutional neural network inferencing for screening red blood cell disease

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