Recognition and Arrangement of Blood Cancer from Microscopic Cell Pictures Utilizing Support Vector Machine K-Nearest Neighbor and Deep Learning

Paswan, Sachin; Rathore, Yogesh

doi:10.1109/ic3iot.2018.8668141

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Alongside the usage of imaging techniques, many studies employed other techniques; for example, Moraes et al [ 133 ] suggested the usage of flow cytometry data for distinguishing leukemia/lymphoma, and Mahmood et al [ 143 ] directed their research to focus more on identifying the most discriminatory features for CLL using patient laboratory test results, demographic parameters, and training a Classification and Regression Trees model on 94 pediatric patients, which was evaluated using 10-fold cross validation. Moreover, both Dharani and Hariprasath [ 31 ] and Jagadev and Virani [ 34 ] used SVM to classify leukemia and its subtypes, while Paswan and Rathore [ 28 ] used K-nearest neighbors to separate blasted blood cells from normal ones and classify them further into either AML or ALL using a value of K=4. By contrast, Moraes et al [ 133 ] suggested the implementation of decision tree as an ML-based technique for distinguishing leukemia/lymphoma, where a binary classification between healthy and immature leukocytes was performed with an 80%/20% data split, followed by a subclassification of immature leukocytes into their respective 4 types using a 70%/30% split, and several combinations of hyperparameters were evaluated during a 5-fold cross validation.…”

Section: Discussionmentioning

confidence: 99%

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A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

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Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

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Section: Discussionmentioning

confidence: 99%

“…Values, n (%) Pathway stage [1,6,7,29,38,40,41,50,51,53,55,64,69,82,92,94,96,[99][100][101][102]105,117,118,136,143,144] 27 (20.6) Prediction [3,10,28,30,[32][33][34]36,[44][45][46]57,58,61,66,71,72,[76][77][78]80,83,85,89,91,…”

Section: Studiesmentioning

confidence: 99%

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Methodologies such as liquid biopsy, which includes extracting circulating tumor DNA or other cancer-related components from blood, provide a minimally invasive method but still a painful and discomforting procedure for the patient [6][7][8]. Furthermore, advances in imaging techniques, such as high-resolution imaging and molecular imaging, allow clinicians to view changes in blood cells and tissues in a non-invasive manner, aiding in the early detection of blood cancer [9][10][11]. Utilizing these non-invasive methods, healthcare providers can diagnose blood cancer in its early stages, allowing for timely treatment and improved patient health.…”

Section: Introductionmentioning

confidence: 99%

Improving blood cancer diagnosis through morphological insights using quantitative phase imaging

Khalid,

Javed,

Cabrera

et al. 2024

Optics, Photonics, and Digital Technologies for Imaging Applications VIII

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Blood cancer remains a major global health challenge, emphasizing the critical need of early diagnosis, for effective treatment and improved patient outcomes. Recently, quantitative phase imaging (QPI) based study of cancerous cell morphology, viability and proliferation, attracts the attention of the pathologist and researchers. In this research article, we have introduced customized QPI based imaging tool for investigation of malignant blood cells for the early detection of cancer. The proposed tool enables the measurement of optical path length variations which gives the provision of labelfree, high-resolution imaging of blood cells, allowing for the precise quantification of cellular parameters such as volume, thickness, and dry mass. The proposed low-cost configuration referred as self-referencing QPI system, makes use of the laser beam for generation of the interferograms. Moreover, this technique has the advantage of numerical focusing, and it is not necessary to place the imaging device at the image plane of the magnifying lens. Therefore, efficient autofocusing feature is designed that ensures the efficacious detection, omitting human error and declining the time-consumption. Moreover, the precision of early cancer diagnosis is enhanced through the integration of convolutional neural network (CNN) and QPI technique, which reduces the likelihood of inaccurate imaging. The non-invasive nature of proposed imaging system minimizes patient discomfort and enables real-time monitoring of disease progression. The methodology demonstrates promising results in the early detection of blood cancer and impassive the need of stained sample preparation. This research contributes to the advancement of personalized medicine and underscores the importance of leveraging quantitative phase imaging for early intervention and improved management of blood cancer.

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Innovative detection of brain tumor using support vector machine classifier and comparison with K-nearest neighbour classifier

Reddy,

Jagadeesh

2023

AIP Conference Proceedings

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Recognition and Arrangement of Blood Cancer from Microscopic Cell Pictures Utilizing Support Vector Machine K-Nearest Neighbor and Deep Learning

Cited by 4 publications

References 15 publications

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Improving blood cancer diagnosis through morphological insights using quantitative phase imaging

Innovative detection of brain tumor using support vector machine classifier and comparison with K-nearest neighbour classifier

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