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.

Библиографические подробности
Главные авторы:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Другие авторы:	Chakrabarty, Amitabha
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2021
Предметы:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Online-ссылка:	http://hdl.handle.net/10361/14725

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