Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

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

doi:10.21742/ijiphm.2016.3.1.01

Cited by 15 publications

(26 citation statements)

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“…The comparison of proposed work with some other existing work, which is considered in scrutiny of proposed work, is described in below table. Authors Accuracy (%) Al-jaboriy [1] 97.01 Vasundhara Acharya [2] 93.15 Sonali Mishra [4] 91.32 Sachin Kumar [9] 95.74 Hossain Abedy [13] 96.35…”

Section: Resultsmentioning

confidence: 99%

“…For detection and classification of the proper stage of the ALL, blood cell blast percentage is a major concern and it is also cooperative in the appropriate treatment of the cancer patients. Three distinctive types of ALL disease are characterized according to FAB (French-American-British) standard based on their structure and the morphological differences among the lymphoblast [4]. The detection and classification of the ALL disease highly depends upon the rightness of the segmentation of human blood cell region (Region of Interest-ROI) from microscopic images for hematologists and pathologists.…”

Section: Introductionmentioning

confidence: 99%

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Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Sharma*¹,

Buksh²

2019

IJEAT

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Blood cell malignantly growth has been accounted for to be one of the most transcendent types of disease maladies. ALL (Acute Lymphoblastic Leukaemias) is the malignant types of blood cancer and their detection and classification in earlier stage is biggest issue. Automatic detection and classification of ALL from microscopic images is a challenging and intellectual assignment in medical science. Existing techniques for ALL detection and classification are an understandable alternative for real-time dermoscopic data analysis. Existing microscopic image processing approaches are unable to analyze the ALL data with non-stationary nature. In this perspective, the focus of this research is to design hybrid Convolutional Neural Network (CNN) architecture by utilizing Firefly Optimization Algorithm (FOA/FFA) to detect the ALL from microscopic images of human blood cell into malignant or normal blood cell. Methods: For training and testing of proposed ALL Detection and Classification (ALL-DC) Model, Standard ALL-IDB (Acute-Lymphoblastic-Leukaemias Image Database for Image Processing) is used with hybrid CNN architecture based on the FOA. Here, Histogram of Oriented Gradients (HOG) descriptor with FOA is used as feature extraction and selection mechanism from the Region of Blood Cell (ROBC).Feature extraction approach plays an important responsibility to classify lots of blood diseases. On the way to achieve this goal, we proposed ALL-DC model that combines recent developments in deep learning with fuzzy based CNN structure and for ROBC segmentation, hybridization of K-means segmentation algorithm with FOA that are capable to segment the accurate blood cell region from microscopic images. Using k-means segmentation technique, the foreground and background component is separated into two regions and after that to improve the segmentation results; FOA is used with the novel concept of image enhancement approach. Results: The proposed ALL-DC system is evaluated using the largest publicly accessible standard ALL-IDB dataset, containing 600 training and 400 testing microscopic images. When the evaluation parameters of proposed work is compared with a number of other state-of-art schemes, the proposed scheme achieves the most excellent performance of 98.5% in terms of accuracy which also known as area under the curve (AUC) in differentiating ALL from benign cell using only the extracted and optimized HOG feature. Conclusion: When the proposed model is tested on different microscopic images, evaluation parameters is calculated and compared with a few other state-of-art methods and we obtained the proposed method achieves the best performance in terms of classification accuracy. ALL-DC model is implemented and constructed using the concept of Image

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Sharma*¹,

Buksh²

2019

IJEAT

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“…Recently, researchers have come out with significant algorithms for splitting rouleaux of RBCs, however still further enhancements are needed to tackle the stated problems precisely. Kumar et al () work show the basic morphological operators for the rouleaux RBCs splitting while the simple morphological operators involve morphology dependent operators like a disc, opening and closing etc., the accuracy of RBCs segmentation will be abruptly disturbed during dense rouleaux of RBCs. Kim et al () splitting process is purely morphologically dependent because they consider the roundness feature of RBCs, is easily disturbed due to a slight pressure on the slide as well as through other diseases.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The word clump means “to glue.” RBCs glued with each other and formed long chains called Rouleaux. The formation of clumps or rouleaux of RBCs occurs due to iron deficiency in the blood (Kumar, Mishra, Asthana, & Pragya, ; Saba, Al‐Zahrani, and Rehman, ). The degree of severance of various diseases is highly dependent on the number of RBCs such as in Malaria the Parasitemia is the ratio of infected RBCs to all RBCs observed on the slide (Muhsin, Rehman, Altameem, Saba, and Uddin, ).…”

Section: Introductionmentioning

confidence: 99%

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection.

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“…In this paper, the focus is on leukemia (blood cancer) detection in microscopic digital images of blood. Leukemia detection is one of the widely studied topics and various methods were proposed in the past (Kumar, Mishra, & Asthana, 2018;Renuga et al, 2016;Singhal, & Singh, 2016). Blood cancer can be diagnosed by analyzing microscopic images of white blood cells.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Optimization (GOA) for Acute Lymphocytic Leukemia Detection

Tuba¹,

Tuba²

2019

SIC

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Computer-aided diagnostic systems represent an active research topic that captures the attention of scientists from various fields. Due to the complexity of human body and the various sources that can be used to generate medical digital images, numerous methods designed for analysing and detecting of different diseases have been developed. In this paper the focus is on detecting acute lymphocytic leukemia in microscopic digital blood images. A method for lymphocyte classification as normal cells or blasts is proposed. For purpose of classification, each cell is described by 11 features, namely 5 shape and 6 texture features. The support vector machine was used as a classifier and its parameters were tuned by means of a novel optimization algorithm: the generative adversarial optimization (GAO) algorithm. No other applications of this optimization algorithm-the latest so far-were found in literature. The proposed method for acute lymphocytic leukemia detection is compared with the Naïve Bayes classificatory, the k-nearest neighbour, the back propagation neural network, and the support vector machine optimized by the bare bones fireworks algorithm. The proposed GAO-based method achieved higher classification accuracy which exhibits the great potential of this new algorithm.

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Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

Cited by 15 publications

References 0 publications

Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

Generative Adversarial Optimization (GOA) for Acute Lymphocytic Leukemia Detection

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