Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar, Mesut; Ergen, Burhan; Sertkaya, Mehmet Emre

doi:10.5755/j01.eie.25.5.24358

Cited by 41 publications

(36 citation statements)

References 24 publications

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“…So, this can be used in further research studies to improve the success rate. The use of PCA along with CNN model did not provide good results [10].…”

Section: J Subclass Separation Of White Blood Cell Images Usingmentioning

confidence: 96%

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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“…So, this can be used in further research studies to improve the success rate. The use of PCA along with CNN model did not provide good results [10].…”

Section: J Subclass Separation Of White Blood Cell Images Usingmentioning

confidence: 96%

A Survey On Automated Methods Used For WBC Classification

Patodia¹,

Nibhasya²,

Chandraprabha³

2020

IJETMS

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“…In this method, suitable shape and color features are extracted by means of the nucleus and the cytoplasm, yet there is no need for the cytoplasm to be segmented. The methods used in [19], [22], [23], [24], [25], and [26] are based on deep learning approaches. Therefore, their models are more complex and have more trainable parameters versus our classifier model which is SVM.…”

Section: Shape and Colormentioning

confidence: 99%

“…. There are some works that have utilized pre-trained CNNs for extracting features in the task of classifying WBCs [17,18,19,20]. In the task of diagnosing acute lymphoblastic leukemia, Rehman et al [17] compared the accuracy of using three different classifiers on the image features extracted by pre-trained CNNs.…”

Section: Introductionmentioning

confidence: 99%

New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images

Tavakoli

Ghaffari

Kouzehkanan

et al. 2021

Preprint

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This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

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“…A project also had demonstrated that Alexnet has the best performance as feature extraction for WBCs type classification in comparison with Lenet, and VGG16 architectures [14]. The Discrete Transform (DT), quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), Support vector machine (SVM), k-nearest neighbors (kNN) with Alexnet also been compared to a softmax classifiers and the highest accuracy is the combination of QDA-Alexnet which is 97.78%.…”

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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Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Cited by 41 publications

References 24 publications

A Survey On Automated Methods Used For WBC Classification

A Survey On Automated Methods Used For WBC Classification

New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

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