A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Khan, Saleem; Sajjad, Muhammad; Hussain, Tanveer; Ullah, Amin; Imran, Ali Shariq

doi:10.1109/access.2020.3048172

Cited by 69 publications

(49 citation statements)

References 126 publications

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“…Moreover, manual classification is time-consuming, complicated, and requires strict professional skills of the inspectors, which cannot meet the requirements of high-efficiency classification tasks on large scales nowadays. Therefore, automatic WBC classification technologies have been extensively developed [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

High-Efficiency Classification of White Blood Cells Based on Object Detection

Yao

Huang

Wei

et al. 2021

Journal of Healthcare Engineering

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White blood cells (WBCs) play a significant role in the human immune system, and the content of various subtypes of WBCs is usually maintained within a certain range in the human body, while deviant levels are important warning signs for diseases. Hence, the detection and classification of WBCs is an essential diagnostic technique. However, traditional WBC classification technologies based on image processing usually need to segment the collected target cell images from the background. This preprocessing operation not only increases the workload but also heavily affects the classification quality and efficiency. Therefore, we proposed one high-efficiency object detection technology that combines the segmentation and recognition of targets into one step to realize the detection and classification of WBCs in an image at the same time. Two state-of-the-art object detection models, Faster RCNN and Yolov4, were employed and comparatively studied to classify neutrophils, eosinophils, monocytes, and lymphocytes on a balanced and enhanced Blood Cell Count Dataset (BCCD). Our experimental results showed that the Faster RCNN and Yolov4 based deep transfer learning models achieved classification accuracy rates of 96.25% and 95.75%, respectively. For the one-stage model, Yolov4, while ensuring more than 95% accuracy, its detection speed could reach 60 FPS, which showed better performance compared with the two-stage model, Faster RCNN. The high-efficiency object detection network that does not require cell presegmentation can remove the difficulty of image preprocessing and greatly improve the efficiency of the entire classification task, which provides a potential solution for future real-time point-of-care diagnostic systems.

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Section: Introductionmentioning

confidence: 99%

High-Efficiency Classification of White Blood Cells Based on Object Detection

Yao

Huang

Wei

et al. 2021

Journal of Healthcare Engineering

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“…These methods are able to mimic human cognition [20,21]. Similarly, these approaches clearly outperform the conventional approaches in the area of denoising [22][23][24].…”

Section: Introductionmentioning

confidence: 89%

“…Since ResNet's first convolution layer has a stride of 2, which gives the feature spatial size of 160 × 160 at the shallowest level, conv1_1 and conv1_2 layers from VGG-16 are used to obtain the full feature size of 320 × 320. Therefore, each hierarchical branch (1 to 6) is then connected to conv1_2 (borrowed from VGG-16), conv1, res2, res3(3), res4 (22), and res5 layers of the ResNet-101, respectively. Axial slices were used where each image has dimension of 256 × 256 × 3.…”

Section: Paired Hierarchical Learning (Phl)mentioning

confidence: 99%

Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

et al. 2021

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Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network CMGDNet for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. CMGDNet uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise.

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“…Traditional machine learning (ML) and deep learning (DL) models have been extensively proposed as alternatives for the automatic classification of leukocytes [ 5 , 31 , 32 ]. Such is the case of Abou et al [ 33 ], who developed a CNN model to identify WBC.…”

Section: State Of the Artmentioning

confidence: 99%

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

et al. 2022

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The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.

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A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Cited by 69 publications

References 126 publications

High-Efficiency Classification of White Blood Cells Based on Object Detection

High-Efficiency Classification of White Blood Cells Based on Object Detection

Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

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