Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Shafique, Sarmad; Tehsin, Samabia

doi:10.1155/2018/6125289

Cited by 45 publications

(28 citation statements)

References 41 publications

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“…Figure 10: Samples of data instances generated from the trained GAN for data augmentation. 8 Computational Intelligence and Neuroscience…”

Section: Resultsmentioning

confidence: 99%

“…The quantitative and qualitative examination of white blood cells reveal a lot about the health of patients. For example, it is possible to assess patients for health conditions including leukaemia, immune system disorders, and cancerous cells [ 8 ]. Conventionally, the identification requires a laboratory setting where acquired images of blood cells are stained using special chemicals (i.e., reagents) and, afterwards, examined under a microscope by a specialist.…”

Section: Introductionmentioning

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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“…Figure 10: Samples of data instances generated from the trained GAN for data augmentation. 8 Computational Intelligence and Neuroscience…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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 median filter is a non-liner filter, which is used for removing noise. While, the histogram equalization is a technique that uses image's histogram to improve low or high contrast of the image which makes better distribution of intensities of the image's histogram [14].…”

Section: Pre-processingmentioning

confidence: 99%

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed

Abdulla²

2020

UHD J SCI TECH

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

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“…Actually, this approach works in the gray level and also can work in RGB. We have achieved high accuracy in both approaches when we have applied them in the classification stage [26][27][28][29]. The following algorithm of applying K-MTh before classifications:…”

Section: K-mth Segmentationmentioning

confidence: 99%

Application of K-MTh Algorithm for Accurate Lunge Cancer Detection

Aouf

2019

IJICIS

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The primary goal of this paper is how to detect the lung cancer tumor based on the K-means clustering thresholding (K-MTh) segmentation technique, rate of death from lung cancer diseases is increased in the last years so the discovered of lung cancer early can protect a lot of people from died. The technique of image processing is utilized ,in image processing ,there are a lot of steps are done for improving the performance of medical diagnostic machine .The technique which used is considered very important to classify the degree of a tumor by improving the thresholding technique before using the classification methods such as support vector machine (SVM). Actually, we have applied Gabor Gaussian filtration method to improve and denoise the CT-image, then we applied the segmentation method (K-MTh) and SVM. Finally, the system has been achieved accuracy more than have been expected for classification method after applying K-MTh (more than 90%).

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Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Cited by 45 publications

References 41 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

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Application of K-MTh Algorithm for Accurate Lunge Cancer Detection

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