Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images

Rawat, Jiledar; Singh, Annapurna; Bhadauria, H.S.; Virmani, Jitendra

doi:10.1016/j.procs.2015.10.113

Cited by 110 publications

(50 citation statements)

References 17 publications

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“…In addition, handsearching of relevant studies and the top Google Scholar results yielded 40 studies for full‐text review. After removing duplicates, twenty‐three (23) studies satisfied the inclusion criteria (see Figure ). The studies were classified according to the type of leukemia into: ALL (13), AML (8), CLL (3), and CML (1).…”

Section: Resultsmentioning

confidence: 99%

“…Compared to other leukemia subsets, pathology diagnosis of ALL was the subset with the higher number of studies. Of the included studies, 13 studies investigated the role of ML tools in ALL diagnosis, with 12 studies applied ML tools on microscopic diagnosis and one study applied them on flow cytometric diagnosis . Seven of the included studies, applying ML on microscopic diagnosis, used only peripheral blood smears, with four studies using bone marrow slides along with blood smears.…”

Section: Resultsmentioning

confidence: 99%

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Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Salah

Muhsen

Salama

et al. 2019

Int J Lab Hematology

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Augmented human intelligence (AHI) and artificial intelligence (AI) tools might shape the future of medical practice. The expansion of data generated by our systems, medical literature, and the inefficiencies of healthcare systems will necessitate utilizing the power of AI tools. 1,2 The integration of AHI tools into medical practice, including machine learning (ML) and deep learning algorithms, has begun. For instance, the United States food and drug administration (US-FDA) has approved many AI-based softwares since 2017 for medical use. 2,3 The introduction of digital pathology has brought many opportunities to the field of pathology, such as telemedicine. 4,5 Recently, the use of digital pathology has allowed for the use of ML (including deep learning algorithms) in the automation of pathological diagnosis. 6,7 The challenges facing the use of ML in pathology are many, including digitalizing slides, labeling in case of Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically. K E Y W O R D S diagnosis, digital, leukemia, machine learning, pathology How to cite this article: Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. Int J Lab Hematol. 2019;41:717-725. https ://doi.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Salah

Muhsen

Salama

et al. 2019

Int J Lab Hematology

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“…In that case, the pixels were segmented through learning by training method, for the sake of ALL identification. Jyoti Rawat et al [5] reported a classification method using GLCM-texture features and shape features along with the SVM classifier, which shows better classification accuracy rate of 89.8% than was the one accomplished by applying them separately. Morteza Moradi Amin et al [6] examined k-means clustering in partitioning the pixels of blood cell images into four distinct clusters, using the H and S components of the HSV color space.…”

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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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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“…In this technique the texture can be modelled as two dimensional gray level variations which can be modelled as two dimensional arrays. The frequency of different combinations of pixel values occur in an image can be tabulate as co-occurrence matrix [12].The GLCM texture features include energy, correlation, sum, contrast, variance, average, homogeneity (inverse difference moment),sum variance, difference variance, sum entropy, difference entropy ,entropy and information measures of correlation describes the texture of the image [2]. For leukocytes the texture features are basically tabulated separately for both nuclei and cytoplasm.…”

Section: Gray Level Co-occurrence Matrix(glcm)mentioning

confidence: 99%

Feature Extraction Techniques for Leukocyte Classification - A Review

Baby¹,

Devaraj²

2018

IJET

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Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images

Cited by 110 publications

References 17 publications

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

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

Feature Extraction Techniques for Leukocyte Classification - A Review

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