White blood cells detection and classification based on regional convolutional neural networks

Kutlu, Hüseyin; Avcı, Engin; Özyurt, Fatih

doi:10.1016/j.mehy.2019.109472

Cited by 210 publications

(128 citation statements)

References 23 publications

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“…Other areas applications of microscope slide classification include the identification of the white blood cells. Kutlu et al [241] created a system classifying different types of white blood cell types (lymphocytes, monocytes, eosinophils, basophils, and neutrophils). The model based on the Regional-CNN ResNet50 framework used 5000 slide images compiled from two datasets-Blood Cell Images (BCCD) and Leukocyte (LISC).…”

Section: -30 Ratiomentioning

confidence: 99%

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Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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Section: -30 Ratiomentioning

confidence: 99%

“…Adapted with permission. [241] Copyright 2020, Elsevier Ltd. b) A smart sensor system uses images of mesenchymal stem cells to count individual amounts of cells in a stem cell cluster. The system based on a CNN smart model.…”

Section: -30 Ratiomentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

View full text Add to dashboard Cite

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“…In future studies, the system ought to be implemented with real-time images by training the system with different medical images and different CNN models. It was noted that, in R-CNN for each region CNN was applied separately so that the training time was almost 84 hours which won't be useful in real-time applications [6].…”

Section: F White Blood Cells Detection and Classification Based On Rmentioning

confidence: 99%

A Survey On Automated Methods Used For WBC Classification

Patodia¹,

Nibhasya²,

Chandraprabha³

2020

IJETMS

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The conventional process of the well-known Complete Blood Count test or abbreviated as CBC test has provided pathologists and hematologists with information for diagnosing blood related diseases. This manual method has proved to be tedious, bias and inaccurate. Thus, automated methods for blood cell detection and counting have been sought out to improve the accuracy for better diagnosis. These automated methods lead to early detection of blood related diseases such as anemia, leukemia, lymphoma and other infections in the immune system. To find an alternative to the manual methods, this paper provides a comparative table of 12 papers which were thoroughly surveyed to draw conclusions from their automated methodologies used and final outcomes. This paper can be useful in the future for knowing which methods gave the best and highest accuracies and further advancements can be done based on the inferences made from the literature survey done in this paper. It's just a bird's overview of the diagnosis of blood cells and their applications in the medical field.

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“…The detection and classification method of the WBCs has been studied widely in medical and also engineering field. One of the study presented a Regional Convolutional Neural Network (RCNN) which was trained by transfer learning using Alexnet, VGG16, Googlenet and Resnet50 [12]. The Resnet50 transfer learning shows the highest performance.…”

Section: Related Workmentioning

confidence: 99%

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

Rohaziat¹,

Razali²,

Nurshazwani³

et al. 2020

IJACSA

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There are several types of blood cancer. One of them is Leukaemia. This is due to leukocyte or white blood cell (WBCs) production problem in the bone marrow. Detection at earlier stage is important so that the patient is able to get a proper treatment. The conventional detection and blood count method is less efficient and it is done manually by pathologist. Thus, there will be a long line to wait for the results and also delay the treatment. A faster detection procedure and technique will have high impact on the real time diagnostic. Fortunately, these problems are able to overcome by making the blood test procedures automatic. One of the effort is the development of deep learning for WBCs detection and classification. In computer aided WBCs detection, the You Only Look Once (YOLO) based platform present a promising outcome. However, the investigation of optimal YOLO structure remains vague. This paper investigate the effect of the deep learning based WBCs detection using You Only Look Once version 3 (YOLOv3) with different pretrained Convolutional Neural Network (CNN) model. The models that been tested are the Alexnet, Visual Geometry Group 16 (VGG16), Darknet19 and the existing YOLOv3 feature extraction model, the Darknet53. The architecture consist of the bounding box for class prediction, feature extraction, and additional convolutional layers. It was trained with 242 WBCs images from Local Initiatives Support Corporation (LISC) dataset. The final outcome shows that the YOLOv3 architecture with Alexnet as its feature extractor produced the highest mean average precision of 98% and have better performance than the other models.

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White blood cells detection and classification based on regional convolutional neural networks

Cited by 210 publications

References 23 publications

Machine Learning‐Enabled Smart Sensor Systems

Machine Learning‐Enabled Smart Sensor Systems

A Survey On Automated Methods Used For WBC Classification

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

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