Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms

Rajpurohit, Subhash; Patil, Sanket; Choudhary, Nitu; Gavasane, Shreya; Kosamkar, Pranali

doi:10.1109/icacci.2018.8554576

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…Similarly, a true negative is an outcome where the model correctly predicts the negative class. Furthermore, a false positive is an outcome where the model incorrectly predicts the positive class, and a false negative is an outcome where the model incorrectly predicts the negative class [5]. Table 7 shows the confusion matrix from k-NN.…”

Section: Resultsmentioning

confidence: 99%

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Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Prakisya¹,

Liantoni²,

Aristyagama³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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Section: Resultsmentioning

confidence: 99%

“…One way to identify AML is to make observations manually, which is quite a time consuming [5]. However, this method is also vulnerable to misidentification.…”

Section: Introductionmentioning

confidence: 99%

Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Prakisya¹,

Liantoni²,

Aristyagama³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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“…These methods aim at detecting the intensity discontinuities in the image to delineate the cellular boundaries. The edge detectors such as Sobel and Canny have been used to determine nucleus boundary in many studies including [58,68,82] and [33,70,83], respectively. Many researchers [37,66] have used a mix of edge detectors for leukocyte segmentation.…”

Section: Segmentationmentioning

confidence: 99%

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

et al. 2022

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Leukemia, the cancer of blood-forming tissues, becomes fatal if not detected in the early stages. It is detected through a blood smear test that involves the morphological analysis of the stained blood slide. The manual microscopic examination of slides is tedious, time-consuming, error-prone, and subject to inter-observer and intra-observer bias. Several computerized methods to automate this task have been developed to alleviate these issues during the past few years. However, no exclusive comprehensive review of these methods has been presented to date. Such a review shall be highly beneficial for novice readers interested in pursuing research in this domain. This paper fills the void by presenting a comprehensive review of 149 papers detailing the methods used to analyze blood smear images and detect leukemia. The primary focus of the review is on presenting the underlying techniques used, their reported performance, along with their merits and demerits. It also enumerates the research issues that have been satisfactorily solved and open challenges still existing in the domain.

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“…Therefore, the use of digital image pattern recognition in leukemia is applied to assist pathologists in accelerating the process of identifying immature white blood cells. (Rajpurohit, Patil, Choudhary, Gavasane, & Kosamkar, 2018). Several studies in the field of computer vision apply various segmentation algorithms for leukemia cases.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

Prakisya

Setiawan

2021

IJIE

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Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms

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Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms

Cited by 31 publications

References 15 publications

Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

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