Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers

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

doi:10.1007/s11042-017-4478-3

Cited by 89 publications

(44 citation statements)

References 31 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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“…By using this method, they were able to achieve accuracy of 98%. Rawat et al [ 15 ] were able to achieve higher accuracy of 99% as compared to all previous methods by using powerful hybrid hierarchical classifiers. Histogram equalization and order statistic filter were used for contrast adjustment and noise removal.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Shafique

Tehsin

2018

Computational and Mathematical Methods in Medicine

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Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.

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“…This is considered as the base work for this proposal. Jyothi Rawat et al [10] designed a methodology for segmenting the nucleus of WBC using Otsu's thresholding with morphological operators. In their work, they classified the types of ALL in three phases: classification of normal and cancerous cells, classification of L1 from other ALL types, classification of L2 and L3.…”

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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Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers

Cited by 89 publications

References 31 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

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

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

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