An automated malaria cells detection from thin blood smear images using deep learning
https://doi.org/10.47665/tb.40.2.013
- Author:
Sukumarran, D.
1
;
Hasikin, K.
1
,
2
;
Mohd Khairuddin, A.S.
2
,
3
;
Ngui, R.
4
;
Wan Sulaiman, W.Y.
4
;
Vythilingam, I.
4
;
Divis, P.C.S.
5
Author Information
1. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia&
2. Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
3. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia&
4. Department of Para-Clinical Sciences, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak
5. Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Sarawak, Malaysia
- Publication Type:Journal Article
- Keywords:
Malaria;
Yolov4;
Faster R-CNN;
SSD300;
deep learning.
- From:Tropical Biomedicine
2023;40(No.2):208-219
- CountryMalaysia
- Language:English
-
Abstract:
Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic
examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films
are examined annually. However, this method’s effectiveness depends on the trained microscopist’s
skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to
determine the most suitable deep-learning object detection architecture and their applicability to detect
and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors
Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and
from four stages of infection with 80/20 train and test data partition. The performance of object
detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The
best-performing model was also assessed with an independent dataset to verify the models’ ability
to generalize in different domains. The results show that upon training, the Yolov4 model achieves a
precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold
of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin
blood smear images. Object detectors can complement a deep learning classification model in detecting
infected cells since they eliminate the need to train on single-cell images and have been demonstrated
to be more feasible for a different target domain.
- Full text:8.2023my1409.pdf
