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.

Bibliographische Detailangaben
Hauptverfasser:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Weitere Verfasser:	Chakrabarty, Amitabha
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2021
Schlagworte:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Online Zugang:	http://hdl.handle.net/10361/14725

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