Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp-Reference-Cited by-同舟云学术

Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp

Published:2022 Issue: Volume:10 Page:102157-102172
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Koirala Anand¹^ORCID,Jha Meena²^ORCID,Bodapati Srinivas³,Mishra Animesh⁴^ORCID,Chetty Girija⁵^ORCID,Sahu Praveen Kishore⁶,Mohanty Sanjib⁶,Padhan Timir Kanta⁶,Mattoo Jyoti⁷,Hukkoo Ajat³

Affiliation:

1. Centre for Intelligent Systems, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia

2. Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Sydney, NSW, Australia

3. Intel Corporation, Santa Clara, CA, USA

4. NVIDIA Corporation, Santa Clara, CA, USA

5. Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia

6. Community Welfare Society Hospital, Rourkela, Odisha, India

7. Intel Technology India Pvt. Ltd., Bengaluru, Karnataka, India

Funder

Australia India Council Grant through Australian Government Department of Foreign Affairs and Trade

Center for Intelligent Systems, Central Queensland University, Australia

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/ielx7/6287639/9668973/09896857.pdf?arnumber=9896857

Reference27 articles.

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3. You Only Look Once: Unified, Real-Time Object Detection

4. SSD: Single shot MultiBox detector;liu;Proc Eur Conf Comput Vis,2016

5. YOLO9000: Better, Faster, Stronger

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