Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Detecting red blood cells and platelets in blood smears using a single multi-class object detector

Burfield, Timothy LU and Carlsson, Sophia (2022) In Master's Theses in Mathematical Sciences FMAM05 20221
Mathematics (Faculty of Engineering)
Abstract
Blood analysis is an integral part of diagnostic medicine and used in most medical fields. The concentrations of red blood cells and platelets, and ratio between these, are used for diagnosing several diseases. CellaVision develops machines and software for automatically capturing images of blood sample smears and detecting its cellular contents. The company currently has separate algorithms for detecting red blood cells and platelets. The aim of this master’s thesis is to develop an object detection model that simultaneously detects these blood cell types, with suciently high accuracy and speed for use on CellaVision systems.

The object detection model YOLOv5 was selected to develop the detector. Several model parameters, hyper... (More)
Blood analysis is an integral part of diagnostic medicine and used in most medical fields. The concentrations of red blood cells and platelets, and ratio between these, are used for diagnosing several diseases. CellaVision develops machines and software for automatically capturing images of blood sample smears and detecting its cellular contents. The company currently has separate algorithms for detecting red blood cells and platelets. The aim of this master’s thesis is to develop an object detection model that simultaneously detects these blood cell types, with suciently high accuracy and speed for use on CellaVision systems.

The object detection model YOLOv5 was selected to develop the detector. Several model parameters, hyper parameters and improvement techniques were evaluated, and the ones maximising the performance were selected. Image augmentations proved to be the most important improvement technique added during development in terms of detection performance. Pseudo labelling was successfully used for creating a large training data set. The results obtained show that it is possible to combine red blood cell and platelet detections in a single object detector with higher speed than when using separate algorithms. Comparing performance with the current individual algorithms, platelet detection was almost as good and red blood cell counting significantly better when using the detector developed during this thesis. (Less)
Popular Abstract (Swedish)
I examensarbetet har maskininlärning använts för att kombinera detektionen av röda blod-kroppar och blodplättar. En snabbare lösning i jämförelse med dagens separata algoritmer på företaget utformades, med prestanda i samma klass.
Please use this url to cite or link to this publication:
author
Burfield, Timothy LU and Carlsson, Sophia
supervisor
organization
course
FMAM05 20221
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3469-2022
ISSN
1404-6342
other publication id
2022:E20
language
English
id
9098683
date added to LUP
2022-09-01 13:36:51
date last changed
2022-09-01 13:36:51
@misc{9098683,
  abstract     = {{Blood analysis is an integral part of diagnostic medicine and used in most medical fields. The concentrations of red blood cells and platelets, and ratio between these, are used for diagnosing several diseases. CellaVision develops machines and software for automatically capturing images of blood sample smears and detecting its cellular contents. The company currently has separate algorithms for detecting red blood cells and platelets. The aim of this master’s thesis is to develop an object detection model that simultaneously detects these blood cell types, with suciently high accuracy and speed for use on CellaVision systems.

The object detection model YOLOv5 was selected to develop the detector. Several model parameters, hyper parameters and improvement techniques were evaluated, and the ones maximising the performance were selected. Image augmentations proved to be the most important improvement technique added during development in terms of detection performance. Pseudo labelling was successfully used for creating a large training data set. The results obtained show that it is possible to combine red blood cell and platelet detections in a single object detector with higher speed than when using separate algorithms. Comparing performance with the current individual algorithms, platelet detection was almost as good and red blood cell counting significantly better when using the detector developed during this thesis.}},
  author       = {{Burfield, Timothy and Carlsson, Sophia}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Detecting red blood cells and platelets in blood smears using a single multi-class object detector}},
  year         = {{2022}},
}