Performance comparison of CNN architectures for detecting Malaria diseases

Performance comparison of CNN architectures for detecting Malaria diseases

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.

Dettagli Bibliografici
Autori principali:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Altri autori:	Chakrabarty, Amitabha
Natura:	Tesi
Lingua:	English
Pubblicazione:	Brac University 2021
Soggetti:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Accesso online:	http://hdl.handle.net/10361/14725

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