Autonomous interpreting peripheral blood film based on deep learning algorithm

The peripheral blood film (PBF) is a laboratory work-up that involves cytology of peripheral blood cells smeared on a slide. As basic as it is, PBF is invaluable in the characterization of various clinical diseases as the PBF is an informative haematological tool at the clinician’s disposal in scree...

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Main Author:	Salehuddin, Nur Anisah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93018/1/NurAnisahSalehuddinMSKE2020.pdf
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id	my-utm-ep.93018
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spelling	my-utm-ep.930182021-11-07T06:00:29Z Autonomous interpreting peripheral blood film based on deep learning algorithm 2020 Salehuddin, Nur Anisah TK Electrical engineering. Electronics Nuclear engineering The peripheral blood film (PBF) is a laboratory work-up that involves cytology of peripheral blood cells smeared on a slide. As basic as it is, PBF is invaluable in the characterization of various clinical diseases as the PBF is an informative haematological tool at the clinician’s disposal in screening, diagnosis and monitoring of disease progression and therapeutic response. Common clinical indication for PBF includes unexplained cytopenia, anaemia, unexplained jaundice, chronic myeloid leukaemia, suspected organ failure such as renal disease, liver failure, lymphoma and chronic lymphocytic leukaemia. PBF can only be interpreted under the microscope. A quick assessment of a PBF can be made within 3 minutes by a skilled laboratory physician but an abnormal film would require a longer time for wider view and differential cell counts. In addition, with the increasing amount of PBF screening (up to hundreds) samples requested per day, it is impossible for the laboratory physician to finish up the PBF screening within the given time frame. Besides, this conventional method tends to give inconsistent outcome as well as poor accuracy due to the significant level of inter-observer variation in grading. In Malaysia particularly, the PBF screening only available in selected General Hospital who has Hematopathology unit. Thus, all PBF samples from Klinik Kesihatan and District Hospital will be sent out to this hospital. The process itself is time consuming and tedious. Therefore, this project is aimed for the PBF to be analysed by a system that could differentiate the component on PBF which are, red blood cell (RBC), white blood cell (WBC) and platelets quantitively. Faster R-CNN algorithm for object detection is implemented as the deep learning framework for training, validating and testing the PBF images. The framework is built by integrating the Keras object detection package on top of backbone, Tensorflow library with Python as the programming language. 2020 Thesis http://eprints.utm.my/id/eprint/93018/ http://eprints.utm.my/id/eprint/93018/1/NurAnisahSalehuddinMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135903 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering 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 Salehuddin, Nur Anisah Autonomous interpreting peripheral blood film based on deep learning algorithm
description	The peripheral blood film (PBF) is a laboratory work-up that involves cytology of peripheral blood cells smeared on a slide. As basic as it is, PBF is invaluable in the characterization of various clinical diseases as the PBF is an informative haematological tool at the clinician’s disposal in screening, diagnosis and monitoring of disease progression and therapeutic response. Common clinical indication for PBF includes unexplained cytopenia, anaemia, unexplained jaundice, chronic myeloid leukaemia, suspected organ failure such as renal disease, liver failure, lymphoma and chronic lymphocytic leukaemia. PBF can only be interpreted under the microscope. A quick assessment of a PBF can be made within 3 minutes by a skilled laboratory physician but an abnormal film would require a longer time for wider view and differential cell counts. In addition, with the increasing amount of PBF screening (up to hundreds) samples requested per day, it is impossible for the laboratory physician to finish up the PBF screening within the given time frame. Besides, this conventional method tends to give inconsistent outcome as well as poor accuracy due to the significant level of inter-observer variation in grading. In Malaysia particularly, the PBF screening only available in selected General Hospital who has Hematopathology unit. Thus, all PBF samples from Klinik Kesihatan and District Hospital will be sent out to this hospital. The process itself is time consuming and tedious. Therefore, this project is aimed for the PBF to be analysed by a system that could differentiate the component on PBF which are, red blood cell (RBC), white blood cell (WBC) and platelets quantitively. Faster R-CNN algorithm for object detection is implemented as the deep learning framework for training, validating and testing the PBF images. The framework is built by integrating the Keras object detection package on top of backbone, Tensorflow library with Python as the programming language.
format	Thesis
qualification_level	Master's degree
author	Salehuddin, Nur Anisah
author_facet	Salehuddin, Nur Anisah
author_sort	Salehuddin, Nur Anisah
title	Autonomous interpreting peripheral blood film based on deep learning algorithm
title_short	Autonomous interpreting peripheral blood film based on deep learning algorithm
title_full	Autonomous interpreting peripheral blood film based on deep learning algorithm
title_fullStr	Autonomous interpreting peripheral blood film based on deep learning algorithm
title_full_unstemmed	Autonomous interpreting peripheral blood film based on deep learning algorithm
title_sort	autonomous interpreting peripheral blood film based on deep learning algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93018/1/NurAnisahSalehuddinMSKE2020.pdf
_version_	1747818627974823936

Autonomous interpreting peripheral blood film based on deep learning algorithm

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