Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM-Reference-Cited by-同舟云学术

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

Published:2020-09-07 Issue:2 Volume:84 Page:202-216
ISSN:1059-910X
Container-title:Microscopy Research and Technique
language:en
Short-container-title:Microsc Res Tech

Author:

Khan Muhammad Attique¹^ORCID,Qasim Muhammad¹,Lodhi Hafiz Muhammad Junaid¹,Nazir Muhammad¹,Javed Kashif²,Rubab Saddaf³,Din Ahmad⁴,Habib Usman⁵

Affiliation:

1. Department of Computer Science HITEC University Museum Road, Taxila Pakistan

2. Department of Robotics SMME NUST Islamabad Pakistan

3. Military College of Signals NUST Islamabad Pakistan

4. Department of CS COMSATS University Islamabad Abbottabad Pakistan

5. Department of Computer Science FAST‐ National University of Computer & Emerging Sciences (NUCES) Chiniot‐Faisalabad Campus, Faisalabad‐Chiniot Road Faisalabad Punjab Pakistan

Publisher

Wiley

Subject

Medical Laboratory Technology,Instrumentation,Histology,Anatomy

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jemt.23578

Reference41 articles.

1. Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion

2. Al‐Dulaimi K. Chandran V. Banks J. Tomeo‐Reyes I. &Nguyen K.(2018).Classification of white blood cells using bispectral invariant features of nuclei shape. Paper presented at the 2018 Digital Image Computing: Techniques and Applications (DICTA).

3. Al‐Dulaimi K. Nguyen K. Banks J. Chandran V. &Tomeo‐Reyes I.(2018).Classification of White Blood Cells Using L‐Moments Invariant Features of Nuclei Shape. Paper presented at the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ).

4. A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition;Arshad H.;Expert Systems,2020

5. Detection of red and white blood cells from microscopic blood images using a region proposal approach

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