Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting

Chibuta, Samson; Acar, Aybar C.

doi:10.1007/s10278-019-00284-2

Cited by 31 publications

(18 citation statements)

References 17 publications

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“…Despite their success in several applications, the state-of-the-art deep learning based one-stage and two-stage object detection algorithms have not been extensively studied for the detection of malaria parasite in microscopic images. The work reported in [6] uses a modified YOLOv3 architecture to detect p.falciparum parasite from thick blood smear microscopic image taken with a digital microscope and smartphone camera. The modified model was obtained by replacing the feature extraction part with convolutional bottleneck residual blocks and removing some of the convolutional layers at the detection part of the original YOLOv3 architecture.…”

Section: The State-of-the-art One-stage and Two-stage Object Detectionmentioning

confidence: 99%

“…Despite their success, the state of the art deep learning based one-stage and two-stage object detection algorithms are not investigated by researchers for detection of malaria parasite. There is one study that attempted to use modified YOLOV3 architecture for the detection of malaria parasite [6]. The model was proposed to make the detection process computationally lightweight so that it can be deployed on mobile devices.…”

Section: Introductionmentioning

confidence: 99%

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Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Abdurahman

Anlay

Aliy

2020

Preprint

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Background Information: Manual microscopic examination is still the "golden standard" for malaria diagnosis. The challenge in the manual microscopy is the fact that its accuracy, consistency and speed of diagnosis depends on the skill of the laboratory technician. It is difficult to get highly skilled laboratory technicians in the remote areas of developing countries. In order to alleviate this problem, in this paper, we propose and investigate the state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from thick blood slides. Methods: YOLOV3 and YOLOV4 are state-of-the-art object detectors both in terms of accuracy and speed; however, they are not optimized for the detection of small objects such as malaria parasite in microscopic images. To deal with these challenges, we have modified YOLOV3 and YOLOV4 models by increasing the feature scale and by adding more detection layers, without notably decreasing their detection speed. We have proposed one modified YOLOV4 model, called YOLOV4-MOD and two modified models for YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. In addition, we have generated new anchor box scales and sizes by using the K-means clustering algorithm to exploit small object detection learning ability of the models.Results: The proposed modified YOLOV3 and YOLOV4 algorithms are evaluated on publicly available malaria dataset and achieve state-of-the-art accuracy by exceeding the performance of their original versions, Faster R-CNN and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. For 608 x 608 input resolution YOLOV4-MOD achieves the best detection performance among all the other models with mAP of 96.32%. For the same input resolution YOLOV3-MOD2 and YOLOV3-MOD1 achieved mAP of 96.14% and 95.46% respectively. Conclusions: Th experimental results demonstrate that the performance of the proposed modified YOLOV3 and YOLOV4 models are reliable to be applied for detection of malaria parasite from images that can be captured by smartphone camera over the microscope eyepiece. The proposed system can be easily deployed in low-resource setting and it can save lives.

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Section: The State-of-the-art One-stage and Two-stage Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Abdurahman

Anlay

Aliy

2020

Preprint

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“…Malaria microscopy is thus a high-value target for automated image-processing and machine learning (ML) systems because such systems can potentially be widely deployed, mitigating the expert-training bottleneck, and because their results are reproducible. Since a thorough review in 2018 [ 4 ], there have been several additional reports proposing or evaluating systems for automated interpretation of malaria blood films [ 5 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

Horning

Delahunt

Bachman

et al. 2021

Malar J

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Background Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated. Methods The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set. Results The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set. Conclusions EasyScan GO’s expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

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“…Digital Holographic microscopy has succeeded in achieving high-sensitivity parasite detection by combining complex microscopy instrumentation with specialized microfluidic devices [16]. Meanwhile, there are a growing number of efforts to process conventional Giemsa-stained blood smear images using deep learning [7,[17][18][19][20][21][22][23][24], including the use of Fourier Ptychographic Microscopy (FPM) with extended depth of focus and numerical aperture [25][26][27]. Notably, low-cost mobile devices and computation hardware have been leveraged to efficiently process thick blood smears [24].…”

Section: Introductionmentioning

confidence: 99%

Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

et al. 2021

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Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.

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Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting

Cited by 31 publications

References 17 publications

Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

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