Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features

Author:

Abbasi Mohamadreza1,Kermani Saeed2,Tajebib Ardeshir3,Moradi Amin Morteza4,Abbasi Manije5

Affiliation:

1. Isfahan University of Medical SciencesP.O. Box 319, Hezar‐Jerib Ave.Isfahan81746 73461Iran

2. Faculty of advanced TechnologiesIsfahan University of Medical SciencesP.O. Box 319, Hezar‐Jerib Ave.Isfahan81746 73461Iran

3. School of Medicine, Isfahan University of Medical SciencesP.O. Box 319, Hezar‐Jerib Ave.Isfahan81746 73461Iran

4. Department of Medical Physics and Biomedical EngineeringSchool of Medicine, Tehran University of Medical Sciences (TUMS)TehranIran

5. Islamic Azad UniversityNorth Tehran Branch, Vafadar Blvd., Shahid Sadoughi St., Hakimiyeh Exit, Shahid Babaee HighwayTehran1651153311Iran

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference28 articles.

1. National Cancer Institute: ‘Cancer stat facts: leukemia – acute lymphocytic leukemia (ALL) 2018’. Available athttps://seer.cancer.gov/statfacts/html/alyl.html

2. Proposals for the Classification of the Acute Leukaemias French-American-British (FAB) Co-operative Group

3. Acute Lymphoblastic Leukemia

4. Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection

5. 2013 Int. Conf. Image Analysis and Processing Naples Italy L. Putzu C. Di Ruberto Investigation of different classification models to determine the presence of leukemia in peripheral blood image

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