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.

Detalles Bibliográficos
Autores principales:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Otros Autores:	Chakrabarty, Amitabha
Formato:	Tesis
Lenguaje:	English
Publicado:	Brac University 2021
Materias:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Acceso en línea:	http://hdl.handle.net/10361/14725

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