NDLI: A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Muhammad Umer Saima Sadiq Muhammad Ahmad Saleem Ullah Gyu Sang Choi Arif Mehmood
Abstract	Malaria refers to a contagious mosquito-borne disease caused by parasite genus plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a person, the parasite multiplies in the host's liver and start destroying the red-cells. The disease is examined visually under the microscope for infected red-cells. This diagnosis depends upon the expertise and experience of pathologists and reports may vary in different laboratories doing a manual examination. Another way around, many machine learning techniques have been applied for spontaneous detection of blood smears. However, feature engineering is a challenging task that requires expertise to adjust positional and morphological features. Therefore, this study proposes a novel Stacked Convolutional Neural Network architecture that improves the automatic detection of malaria without considering the hand-crafted features. The 5-fold cross-validation process on 27, 558 cell images with equal instances of parasitized and uninfected cells on a publicly available dataset from the National Institute of health, the accuracy of our proposed model is 99.98%. Furthermore, the statistical results revealed that the proposed model is superior to the state-of-the-art models with 100% precision, 99.9% recall, and 99% f1-measure.
e-ISSN	21693536
DOI	10.1109/ACCESS.2020.2994810
Journal	IEEE Access
Volume Number	8
Language	English
Publisher	IEEE
Publisher Date	2020-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Convolutional Neural Network (cnn) Malaria Blood Smear Images Deep Learning Diagnostic Approach
Content Type	Text
Resource Type	Article

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