Automatic classification of leukocytes using morphological features and Naïve Bayes classifier

Gautam, Anjali; Singh, Priyanka; Raman, Balasubramanian; Bhadauria, H.S.

doi:10.1109/tencon.2016.7848161

Cited by 47 publications

(17 citation statements)

References 13 publications

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“…Description of the approach (Bikhet et al) [28] Image preprocessing, feature extraction, and classification (Piuri and Scotti) [27] Features extracted, feature selection, and classification (Hiremath et al) [29] Advanced image processing, feature extraction, and classification (Mathur et al) [7] Image processing, feature extraction, and classification (Gautam et al) [30] Feature extraction and Naïve Bayes classifier (Rawat et al) [31] Feature extraction and selection via PCA and classification (Ours- GAN Computational Intelligence and Neuroscience…”

Section: Methodsmentioning

confidence: 99%

“…They posit that F-RVM is easier to train and requires a small time for inference than the Extreme Learning Machine (ELM) and standard RVM. Otsu's thresholding method was used in [ 30 ] for segmenting white blood cells, after which mathematical morphological operations were applied to eliminate all elements that have no resemblance with white blood cells. Following the segmentation results, features were extracted from the cell nucleus for training a Naïve Bayes classifier.…”

Section: Related Workmentioning

confidence: 99%

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Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Almezhghwi

Serte

2020

Computational Intelligence and Neuroscience

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White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Almezhghwi

Serte

2020

Computational Intelligence and Neuroscience

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“…The YOLOv3 detection method which been implemented in this project utilized the fundamental of neural network. This detection method practice a deep learning method for localization step which by using bounding box prediction, instead of the common sliding window search method [6]. The training process uses the sum of squared error loss and the logistic regression analysis for the objects' score prediction.…”

Section: Introductionmentioning

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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“…The CNN method is not conclusively the best as the number of samples is small (115 training and 25 testing). Gautam et al [2] proposed a method to detect leukocytes in human blood. They simply segmented an image by Otsu thresholding and selected the composition that looked like leukocytes by morphing, finally classifying the sample by naive Bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of cells in microscopic fecal images using convolutional neural networks

Liu

Wang

et al. 2019

Bioscience Reports

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The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

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Automatic classification of leukocytes using morphological features and Naïve Bayes classifier

Cited by 47 publications

References 13 publications

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

Automatic classification of cells in microscopic fecal images using convolutional neural networks

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