Automated detection and classification techniques of Acute leukemia using image processing: A review

Bagasjvara, R.G; Candradewi, Ika; Hartati, Sri; Harjoko, Agus

doi:10.1109/icstc.2016.7877344

Cited by 34 publications

(18 citation statements)

References 10 publications

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“…In [7], the main focus is on the geometry of cells like perimeter, area and statistical measures like a standard deviation that helps to separate the leukemic cells from other blood components using various processing tools. Then, after the statistical properties are identified, Leukemia is detected.…”

Section: Previous Workmentioning

confidence: 99%

Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

Arunachalam¹

2020

IJSRCSE

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The cancer of blood and bone marrow is called Leukemia. It is a result of the uncontrolled reproduction of immature white blood cells. It hampers the ability of the body to fight infection. In Leukemia, the white blood cells (WBC) are generally affected. There are different types of Leukemia namely: Acute myelogenous Leukemia (AML), Acute lymphocytic Leukemia (ALL), Chronic myelogenous Leukemia (CML), Chronic lymphocytic Leukemia (CLL). This paper proposes to automate the Leukemia detection process using machine learning and different techniques of image processing. The dataset consists of images of blood smear which is of both Leukemia and non-Leukemia patients. K-means Clustering, Marker-Controlled Watershed segmentation, and HSV colour-based segmentation are the image segmentation algorithms that have been used. Various features from the segmented lymphocyte images are extracted since the structural components of normal and Leukemic lymphocytes differ remarkably. The SVM classifier, which is a machine learning technique, is used to further classify Leukemia into its different types. This paper aims at identifying Leukemia and determine its types whether it is AML, ALL, CML or CLL which takes the classification process one step further as a majority of the previous works have been restricted to just detection of Leukemia or classifying into few of the main subtypes. The proposed system is successfully implemented using MATLAB.

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Section: Previous Workmentioning

confidence: 99%

Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

Arunachalam¹

2020

IJSRCSE

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“…R.G Bagasjvara,Ika Candradewi, Sri Hartati and Agus Harjoko [9] proposed a technique for automatic detection and classification of Acute Leukemia where for segmenting White Blood Cells a dual-threshold method is used and this approach used two threshold values which obtained from golden section search algorithm, where two threshold values were applied in different color space: gray-scale image and HSV color space which gives the accurate count of cells. The limitation here is it cannot classify subtypes of acute cells and chronic cells.…”

Section: Related Workmentioning

confidence: 99%

Identification of Types of Leukemia Influenced Cells and it's Classification

Ewit¹

2018

IJCCN

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Image processing and analysis paves the way to extract and quantify objects and patterns in image data. Microscopy images in biology are often found to be complex and noisy. The diagnosing of blood disorders plays a very crucial role in the timely detection and curing of disease in the medical field and thereby protecting and saving human life. We discuss in this paper, a proposed framework for segmenting the white blood cell using Otsu's global threshold method, and then by routing it to morphological minimum filter the darkest part of the image is identified as 'Nucleus'. Then the image is subjected to the process of feature extraction to obtain its geometric features information such as the size and shape of a nucleus, area, perimeter, eccentricity, circularity and solidity and the classification of cells into types and subtypes of leukemia are explained.

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“…Although some of these proposed methods were found to be faster and more cost effective than manual examination, their impact and accuracy remain insufficient ( Shafique and Thesin, 2018 ). Whereas, Wang et al (2019) achieved a detection speed of 14 to 100 milliseconds by utilizing convolution neural networks and GPU, most proposed methods produce false-negative errors and achieve overall accuracy in the range of 93–98% ( Bagasjvara et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Bodzas

Kodytek

Žídek

2020

Front. Bioeng. Biotechnol.

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Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

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Automated detection and classification techniques of Acute leukemia using image processing: A review

Cited by 34 publications

References 10 publications

Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

Identification of Types of Leukemia Influenced Cells and it's Classification

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

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