Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

Çınar, Ahmet; Tuncer, Seda Arslan

doi:10.1007/s42452-021-04485-9

Cited by 55 publications

(33 citation statements)

References 26 publications

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“…For this reason, most recent works addressing this task exploited CNN-based systems, being more suited to cope with such variability. The authors in [23] performed a concatenation of pre-trained AlexNet and GoogleNet's feature vectors by taking their maximum values. Then, they classify lymphocytes, monocytes, eosinophils, and neutrophils with the Support Vector Machine (SVM) strategy.…”

Section: Related Workmentioning

confidence: 99%

“…Here we used k = 1, computed using the Euclidean distance. The SVM, on the other hand, is one of the most used in biomedical application [12,23,29,30]. Here we use a Gaussian radial basis function (RBF) trained using the one VS rest approach.…”

Section: Classic Machine Learningmentioning

confidence: 99%

“…To this end, we performed several experiments simulating a real-world application scenario in which a model is trained using near-perfect manually annotated BBs and deployed on automatically generated BBs. In the literature, many works deal with the classification of white blood cells [17,[23][24][25][26], also proposing Unsupervised Domain Adaptation methods to address the Domain Shift present between different datasets [27]. However, to the best of our knowledge, no one has ever thoroughly investigated whether and to what extent specific features, such as bounding box size and quality, can be crucial for extracting representative features and creating robust models for WBCs classification.…”

Section: Introductionmentioning

confidence: 99%

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On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Loddo

Putzu

2021

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Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Classic Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Loddo

Putzu

2021

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“…A fully connected dense layer does the classification step to learn the model using the extracted features [ 18 , 19 ]. Following this structure, several types of CNN models have been proposed for specific tasks such as classification, among which are AlexNet [ 20 ], ResNet [ 21 ], VGG [ 22 ], and GoogLeNet [ 23 ], and segmentation, highlighting the fully connected network (FCN) [ 24 ], U-Net [ 25 ], and Faster-RCNN [ 26 ], these being applied in the processing of blood smear images for differential WBC counting, achieving good performance results. Recently, an efficient network architecture called MobileNet was proposed as a small, lightweight, and low latency model for mobile and embedded vision applications [ 27 ].…”

Section: Introductionmentioning

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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“…In their work the input image required pre-processing, segmentation and manual feature extraction based on digital image processing techniques before the classification process which consumes high execution time. Cinar et al [7] performed a comparative classification of leukocytes into lymphocytes, monocytes, eosinophils, and neutrophils, utilizing Alexnet-Googlenet-SVM model. In their work the dimension of feature vector is very high since it combines the feature vectors from two different pre-trained models.…”

Section: Introductionmentioning

confidence: 99%

Leukocyte classification based on feature selection using extra trees classifier: a transfer learning approach

Baby¹,

Devaraj²,

Hemanth³

et al. 2021

Turk J Elec Eng & Comp Sci

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The criticality of investigating the White Blood Cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shaping a physician's assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such as eosinophil, monocyte, lymphocyte and neutrophil based on images from three different datasets. The methodology adopted in this paper is transfer learning approach in which the features are extracted using ResNet50, DenseNet121, MobileNetv2, Inceptionv3 and Xception deep learning models.The extra trees classifier is used as an intermediate stage for selecting most predominant features which reduce the execution time. When evaluating the performance on the basis of recall, precision, F-Measure and accuracy parameters, the classification of ResNet50 features selected by extra trees classifier using multi-class SVM provides the highest accuracy of 90.76% .

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Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

Cited by 55 publications

References 26 publications

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

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

Leukocyte classification based on feature selection using extra trees classifier: a transfer learning approach

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