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A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Computers, Materials & Continua 2022年第9期72卷 5889-5907页

作者： Esraa Hassan Mahmoud Y.Shams Noha A.Hikal Samir Elmougy Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Infectious diseases are an imminent danger that faces human beings around the *** is considered a highly contagious *** diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human ***,there is a need to investigate an efficient and fast automatic diagnosis *** deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image *** study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected *** focuses on detecting infected cells,which aids in the computation of parasitemia,or infection *** proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the *** comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

关键词： Deep learning malaria dataset diagnostic medical approach

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