AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites
نویسندگان
1 Faculty Member, Electrical and Computer Engineering Department, Hakim Sabzevari University, Iran
2 Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, India
3 Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
4 Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
5 Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran
doi
10.61186/ijbc.15.3.112چکیده
Background: Malaria remains a significant global health problem, with a high incidence of cases and a substantial number of deaths yearly. Early identification and accurate diagnosis play a crucial role in effective malaria treatment. However, underdiagnosis presents a significant challenge in reducing mortality rates, and traditional laboratory diagnosis methods have limitations in terms of time ...
