Molecular Markers for Risk Stratification in Adult Acute Myeloid Leukemia with Normal Cytogenetics

Fuchs, Ota

doi:10.5772/19844

Acute Leukemia - The Scientist's Perspective and Challenge 2011

DOI: 10.5772/19844

|View full text |Cite

Molecular Markers for Risk Stratification in Adult Acute Myeloid Leukemia with Normal Cytogenetics

Ota Fuchs¹

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 251 publications

(201 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Matlab Based Potent Algorithm for WBc cancer Detection and classification

Nithyaa¹,

R²,

et al. 2021

Biomed. Pharmacol. J.

View full text Add to dashboard Cite

This paper aims to automate the detection of cancer using digital image processing techniques in MATLAB software. The analysis of white blood cells (WBC) is a powerful diagnostic tool for the prediction of Leukemia. The automatic detection of leukemia is a challenging task, which remains an unresolved problem in the medical imaging field. This Automation in Biological laboratories can be done by extracting the features of the blood film images taken from the digital microscopes and processed using MATLAB software. The aim of this approach is to discover the WBC cancer cells in an earlier stage and to reduce the discrepancies in diagnosis, by improving the system learning methodology. This paper presents the potent algorithm, which will eliminate the dubiety, in diagnosing the cancers with similar symptoms. This Algorithm concentrates on major WBC cancers, such as Acute Lymphocytic Leukemia, Acute Myeloid Leukemia, Chronic Lymphocytic Leukemia and Chronic Myeloid Leukemia. As they are life threatening diseases, rapid and precise differentiation is necessary in clinical settings. These cancers are categorized by segmentation and feature extraction, which will be further, classified using Random forest classification (RFC). RFC will classify the cancer using a decision tree learning method, which uses predictors at each node to make better decision.

show abstract

Matlab Based Potent Algorithm for WBc cancer Detection and classification

Nithyaa¹,

R²,

et al. 2021

Biomed. Pharmacol. J.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Molecular Markers for Risk Stratification in Adult Acute Myeloid Leukemia with Normal Cytogenetics

Cited by 1 publication

References 251 publications

Matlab Based Potent Algorithm for WBc cancer Detection and classification

Matlab Based Potent Algorithm for WBc cancer Detection and classification

Contact Info

Product

Resources

About