DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Applied System Innovation

Aliyu Abubakar¹  Mohammed Ajuji²  Ibrahim Usman Yahya² 

[1] Centre for Visual Computing, University of Bradford, Bradford BD7 1DP, UK;Department of Computer Science, Faculty of Science, Gombe State University, Tudun Wada 760231, Nigeria;

关键词: malaria;    CNN;    deep learning;    SVM;    classification;   

DOI  :  10.3390/asi4040082

来源: DOAJ

【 摘 要 】

Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.

【 授权许可】

Unknown