Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm

Kumar, Sachin; Mishra, Sumita; Asthana, Pallavi; Pragya,

doi:10.1007/978-981-10-3773-3_64

Cited by 58 publications

(21 citation statements)

References 20 publications

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“…The proposed work is evaluated repetitively on a large number of images. To reduce the computational time of GLCM, the number of gray levels of the image is reduced from 256 to 32 levels by dividing the resized image (12) with an appropriate value [19].…”

Section: Resultsmentioning

confidence: 99%

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A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

Umamaheswari

Geetha²

2018

CIT

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Even in this modern era today, life's extent is still being challenged by many pathological diseases such as cancer. One such hazard is leukemia. Even a trivial setback in detecting leukemia lead to a severe outcome: the affected cells may eventually prove to be fatal. To combat this, we propose an algorithm to better segment the nucleus region of White Blood Cells (WBC) found in stained blood smear images with the intent of identifying Acute Lymphoblastic Leukemia (ALL). In our proposal, the image is made ready for segmentation in the preprocessing stage by changing its size, brightness, and contrast. In the segmentation stage, the nucleus region is segmented by mathematical operators and Otsu's thresholding. Then mathematical morphological operators are applied in post-processing stage, which makes the nucleus region convenient for feature extraction. Finally, the segmented regions are classified into ALL affected and regular cells by means of the proposed Customized K-Nearest Neighbor classifier algorithm. This work was experimented with over 80 images of the ALL-IDB2 dataset and attained an accuracy rate of 96.25%, 95% of sensitivity and 97% of specificity.

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Section: Resultsmentioning

confidence: 99%

“…In 2018, Sachin Kumar et al [12] demonstrated a method of segmentation by k-means clustering with the assistance of color, texture, geometrical and shape features of the nucleus. They used KNN and Naïve Bayes classifiers to achieve effective classification with the accuracy rate of 92.8%.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

Umamaheswari

Geetha²

2018

CIT

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“…Experiments on a dataset of 1030 blood smear WBC images produced an average accuracy of 98.6% for this framework. Kumar et al [13] presented an automated detection system for acute leukemia. The system started with the pre-processing of noise and blurring in microscopic digital images.…”

Section: Traditional Methodsmentioning

confidence: 99%

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

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Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.Computers 2020, 9, 29 2 of 12 and classification. However, the success of each step depends on the success of the preceding step. For example, the success of classification depends on the success of the preceding feature extraction, which itself depends on the success of the preceding segmentation. Hence, high classification accuracy requires the success of all steps, each of which is non-trivial and problem-dependent [1,7].Recently, deep learning achieved many breakthroughs in different fields such as computer vision, natural language processing, and object recognition. Deep neural networks, such as convolutional neural networks (CNNs), can be used effectively to build computer-aided diagnostic systems. However, the design and training of deep neural networks are non-trivial and time-consuming tasks. Hence, instead of building a deep neural network from scratch, we use the concept of transfer learning, in which a deep network that achieved success in solving a certain problem is tuned to solve another problem.This paper proposes two classification models that are based on transfer learning and can distinguish between healthy and unhealthy blood smear images with high accuracy. These models employ AlexNet, which is a deep CNN that achieved huge success in the image classification challenge, ImageNet 2012.The remaining sections of this paper are arranged as follows: Section 2 covers some of the traditional and end-to-end-based methods described in the literature. Section 3 presents our two proposed classification models. Section 4 discusses their implementation in our experiments. Section 5 discusses the results obtained. Finally, Section 6 concludes the paper.

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“…In 2018, Kumar et al proposed a new scheme for the detection of leukaemia cells by first applying morphological cleaning to enhance the tested image of the blood. The colour k‐mean clustering is then applied to the L * a * b colour space of the blood image to segment the WBCs from the tested microscopic image [16]. In addition, the bounding box technique is applied on the ( a *) component of the L * a * b colour space to crop blast cells from the microscopic blood images.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.

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Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm

Cited by 58 publications

References 20 publications

A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

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