Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator-Reference-Cited by-同舟云学术

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

Published:2023 Issue: Volume:11 Page:4117-4130
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Omaer Faruq Goni Md.¹^ORCID,Mondal Md. Nazrul Islam²,Islam S. M. Riazul³^ORCID,Nahiduzzaman Md.¹^ORCID,Robiul Islam Md.¹^ORCID,Anower Md. Shamim⁴^ORCID,Kwak Kyung-Sup⁵^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

2. Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

3. Department of Computer Science, University of Huddersfield, Huddersfield, U.K.

4. Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

5. Department of Information and Communication Engineering, Inha University, Incheon, South Korea

Funder

National Research Foundation of Korea-Grant

Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10006828.pdf?arnumber=10006828

Reference56 articles.

1. Increasing the robustness of CNN acoustic models using autoregressive moving average spectrogram features and channel dropout

2. A survey of FPGA-based accelerators for convolutional neural networks

3. Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks

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