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

Umamaheswari, Duraiswamy; Geetha, S.

doi:10.20532/cit.2018.1004123

Cited by 19 publications

(14 citation statements)

References 16 publications

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“…Furthermore, experimental outcomes revealed that the proposed model is more effective than previous works carried out for the classification of ALL. Umamaheswari and Geetha [17] GLCM+Statistical+Geometrical Features C-KNN 96.25 Tuba and Tuba [18] Shape+Texture features GAO-based methods 93.84 Singhal and Singh [15] LBP GLCM -------93.84 87.30 Praveena and Singh [16] GreyJOA…”

Section: Discussionmentioning

confidence: 99%

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Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

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Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.

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Section: Discussionmentioning

confidence: 99%

“…The study Umamaheswari and Geetha [17] developed a scheme for optimized identification and detection of ALL using a novel customized-KNN classification model. During the preprocessing stage of this work, the medical image was prepared for segmentation through changing its size, brightness, and contrast.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

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“…However, the scheme used is SVM, which attained 89.81% accuracy. In [47], the ALL-IDB2 dataset is used for the identification of ALL where the method investigated is customized KNN with 96.25% accuracy. In [48], by utilizing ALL-IDB1 dataset, ALL is classified on the basis of cell energy features by employing SVM, which attained 94.00% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

Bibi

Sikandar

Din

et al. 2020

Journal of Healthcare Engineering

110

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For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

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“…The features are thresholded to filter the leukocytes from some other background blood elements. After sampling a random set of images, the threshold of solidity, area, and perimeter are set as 0.80, 800, and 0.40 respectively (16) . It is done as:…”

Section: Feature Thresholdingmentioning

confidence: 99%

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Umamaheswari¹

2020

IJST

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Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image.

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

Cited by 19 publications

References 16 publications

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

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