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.

Bibliografische gegevens
Hoofdauteurs:	Rinky, Habiba Karim, Bhuiyan, Rakeya Rahim, Rahman, Humayra Tasnim
Andere auteurs:	Chakrabarty, Amitabha
Formaat:	Thesis
Taal:	English
Gepubliceerd in:	Brac University 2021
Onderwerpen:	Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning
Online toegang:	http://hdl.handle.net/10361/14725

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