Automated recognition of white blood cells using deep learning

Khouani, Amin; Daho, Mostafa El Habib; Mahmoudi, Sidi Ahmed; Chikh, Mohammed Amine; Benzineb, Brahim

doi:10.1007/s13534-020-00168-3

Cited by 27 publications

(13 citation statements)

References 24 publications

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“…For the automatic segmentation of WBC, 18 introduced a random forest-RI ensemble methods-based approach for instance and variable selection to filter out noisy and unnecessary information. 19 proposed an improvement of the mask RCNN by adding and adjusting hyper-parameters and spatial information. The leukocyte segmentation step can also be considered as part of a cell recognition process; as in Matek et al 20 article, which uses the ResNeXt CNN architecture for single cells enumeration.…”

Section: Related Workmentioning

confidence: 99%

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Modified U‐Net for cytological medical image segmentation

Benazzouz

Benomar

Moualek

2022

Int J Imaging Syst Tech

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Deep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.

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Section: Related Workmentioning

confidence: 99%

“…For the automatic segmentation of WBC , 18 introduced a random forest‐RI ensemble methods‐based approach for instance and variable selection to filter out noisy and unnecessary information 19 . proposed an improvement of the mask RCNN by adding and adjusting hyper‐parameters and spatial information.…”

Section: Related Workmentioning

confidence: 99%

Modified U‐Net for cytological medical image segmentation

Benazzouz

Benomar

Moualek

2022

Int J Imaging Syst Tech

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“…CNN obtained an accuracy of 96.63% in five types of WBCs classification (25). The dataset proposed by Khouani et (26). Timely proposed CNN and RNN merging model with canonical correlation analysis illustrated an excellent performance of 95.89% to classify four types of WBCs in public data from Shenggan/BCCD data and kaggle.com/ paultimothymooney/blood-cells/data (27).…”

Section: Deep-learning-based Algorithmmentioning

confidence: 99%

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

et al. 2022

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BackgroundIt is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.ObjectiveThis study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.MethodThe cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.ResultsWith respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.ConclusionA deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

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“…Khouani et al [25] proposed a deep learning approach to automatically identify the WBCs from the bone marrow and peripheral blood cell images obtained. Here, the input is preprocessed before sending it to the deep NN.…”

Section: Review Of Related Workmentioning

confidence: 99%

Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

2020

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In this study, an efficient a deep learning architecture‐based peripheral blood cell image recognition and classification is proposed using hybrid disruption‐based salp‐swarm and cat swarm (DSSCS)‐based optimized convolutional neural networks (DSSCSCNNs) method. The DSSCSCNN method is employed to overcome the hyperparameter problem in CNN and it also helps this model to work on small peripheral blood cell data sets. In the DSSCSCNN method, the authors develop a binary coding technique that converts parameter tuning problems into an optimization problem. The original salp swarm algorithm is enhanced using a disruptive operator and salp swarm optimization algorithm to form the novel DSSCS algorithm which increases the diversity of the search space by providing higher classification accuracy. In this study, the CNNs use Vgg‐16 architecture is used for training purposes. The global classification accuracy obtained when trained with the Vgg‐16 model is 97%. This method establishes a fine‐tuning process to develop a classifier trained using 15,976 images acquired from clinical practice. The proposed model gives improved performance in terms of accuracy, specificity, and sensitivity. In the WBC determination, the proposed approach has shown 100% achievement. It also provides the best overall classification accuracy of 99%.

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Automated recognition of white blood cells using deep learning

Cited by 27 publications

References 24 publications

Modified U‐Net for cytological medical image segmentation

Modified U‐Net for cytological medical image segmentation

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

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