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.

Opis bibliograficzny
Główni autorzy:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Kolejni autorzy:	Chakrabarty, Amitabha
Format:	Praca dyplomowa
Język:	English
Wydane:	Brac University 2021
Hasła przedmiotowe:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Dostęp online:	http://hdl.handle.net/10361/14725

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