Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Fathi, Ehsan; Rezaee, Mustafa Jahangoshai; Tavakkoli–Moghaddam, Reza; Alizadeh, Azra; Montazer, A

doi:10.1177/0954411920938567

Cited by 30 publications

(11 citation statements)

References 52 publications

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“…Because the WHO classification has specified the subgroups in more detail and it can be used to obtain the different types of ALL more correctly. 12,13 Today, computer technologies such as artificial intelligence (AI) have received attention from researchers from different branches of science in diagnosing blood diseases, especially ALL. The use of these technologies in the form of different algorithms has conferred amazing results in ALL diagnosis and classification.…”

Section: Introductionmentioning

confidence: 99%

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Ghaderzadeh

Aria

Hosseini

et al. 2021

Int J of Intelligent Sys

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The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from noncancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, Mobile-NetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and Dense-Net201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved

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Section: Introductionmentioning

confidence: 99%

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Ghaderzadeh

Aria

Hosseini

et al. 2021

Int J of Intelligent Sys

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show abstract

“…To design and develop ML algorithms, hematologists have made some of these datasets (that include PBS images) available to researchers. ALL-IDB, one of the most well-known datasets published in two versions, has been utilized in many articles, most of which have diagnosed and classified acute lymphoblastic leukemia (ALL) via different ML techniques [16][17][18][19][20][21]. ere is another published leukemia dataset called Benchmark for the development of ML algorithms, used by some studies.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Ghaderzadeh

Asadi

Hosseini

et al. 2021

Scientific Programming

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Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

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“…Their experiment demonstrated the superior performance of the CART method. Fathi et al [14] produced an integrated approach combining PCA, neuro-fuzzy, and GMDH (group method of data handling) to diagnose ALL, which helps to detect two types of leukemia, such as ALL and acute myeloid leukemia. Kashef et al [39] recommended different ML algorithms, such as decision tree [25], SVM, linear discriminant analysis, multinomial linear regression, gradient boosting machine, RF, and XGBoost [25], where the XGBoost algorithm exhibited the best results.…”

Section: Literature Reviewmentioning

confidence: 99%

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

Mondal¹,

Hasan²,

Jawad³

et al. 2021

Preprint

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Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by numerous immature lymphocytes. Even though automation in ALL prognosis is an essential aspect of cancer diagnosis, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands experienced pathologists to carefully read the cell images, which is arduous, time-consuming, and often suffers inter-observer variations. This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of different deep CNNs to recommend a better ALL cell classifier.The weights for the ensemble candidate models are estimated from their corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We utilize the publicly available C-NMC-2019 ALL dataset to conduct all the comprehensive experiments. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, 1

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Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Cited by 30 publications

References 52 publications

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

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