Counting and classification of white blood cell using Artificial Neural Network (ANN)

Manik, Stevani Rebekka Saulina; Saini, Lalit Mohan; Vadera, Nikhil

doi:10.1109/icpeices.2016.7853644

Cited by 39 publications

(15 citation statements)

References 7 publications

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“…WBC analysis is undeniably important as it helps to prevent disease's complications and an early prevention can be made [2]. Other than that, it is very helpful for diagnosing the patient's health condition [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Safuan¹,

Razali²,

Nurshazwani³

et al. 2020

IJACSA

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Analyzing and classifying five types of Lymphocyte White Blood Cell (WBC) is important to monitor the lack or excessive amount of cell in human body. These harmful amount of cell must be detected early for the early treatment can be run to the patient. However, the process may be tedious and time consuming as it is done manually by the experts. Other than that, it may yield inaccurate result as it depends on the pathologist skill and experience. This work presents a way that can be the second opinion to the experts using computer aided system as a solution. Convolutional Neural Network (CNN) is applied to the system to avoid complex structure and to eliminate the features extraction process. Three CNN models of mobilenet, resnet and VGG-16 is experimented on three different datasets which are kaggle, LISC and IDB-2. Kaggle, LISC and IDB-2 dataset consist of 6000, 242 and 260 images respectively. The result is divided into two parts which are dataset and model. As for IDB-2 dataset, the best model is VGG with training and validation accuracy of 0.9721 and 0.7913 respectively. While for kaggle and LISC dataset, the best model is resnet as it achieved training accuracy of 0.9713 and 0.9771 respectively. The highest validation accuracy for kaggle is 0.5955 and 0.5781 for LISC. Lastly, the best database that is most suitable for all model is IDB-2 database. It obtained highest training and validation accuracy for all model of mobilenet, resnet and VGG-16.

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Section: Introductionmentioning

confidence: 99%

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Safuan¹,

Razali²,

Nurshazwani³

et al. 2020

IJACSA

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“…Then they use k-means clustering to classify the cell, with an accuracy of 67%. Manik et al [5] extracted the cell from a colorful image using the segment and morphology method, then computed eight different features for the ANN, and finally got an accuracy of 98.9% from a total sample of 90. Zhang et al [6] combined two methods to distinguish the leukocytes and impurities.…”

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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“…Purpose of post-processing step is to transform the original color of blood image to a more suitable level of intensity by using various types of method. There are many pre-processing techniques that is used in previous works such as gray-level conversion and contrast stretching [7], adaptive histogram equalization [8] and contrast adjustment [9]. Segmentation is reported to be one of the most crucial parts as the final result is highly dependent on this process [10].…”

Section: Introductionmentioning

confidence: 99%

Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia

Safuan

Tomari

Zakaria

et al. 2019

IJEECS

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Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis which is challenging and time consuming. It can also yield inaccurate result as it is highly dependent on the pathologist’s skills. Industry has come out with hematology counter which is fast, accurate and automated. However, these machines are costly and cannot be afforded by some countries. For that reason, Computer Aided System (CAS) will be a great help to the pathologist for assisting purposes and it also can act as second opinion for the pathologist. This system contains six main steps which are color space correction, WBC segmentation, post processing, clumped area extraction, feature extraction and lymphoblast classification. Firstly, color space correction is apply by using l*a*b* color space to standardize the image’s intensity. Next, WBC segmentation is made to prune out WBC region using color space analysis with Otsu thresholding. However, segmented image contains noises that need to be eliminated and it is accomplished by applying morphological filter with Connected Component Labelling (CCL). There is an overlapping WBC which need to be separated by using Watershed method to extract the individual cells. Next, feature extraction is made to collect the cell’s data to be fed into the classifier. Classifier used in this system to classify lymphoblast is Support Vector Machine (SVM) and this system is able to achieve 96.69% of accuracy.

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Counting and classification of white blood cell using Artificial Neural Network (ANN)

Cited by 39 publications

References 7 publications

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

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

Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia

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