Abstract
Malaria is a life-threatening disease and is a concern of global health threat. The standard way of diagnosing the malaria is by visually examining them under microscope and is very lengthy and tedious task. In this paper, the authors has purposed custom Convolutional Neural Network model for detection of malaria on blood smear slide images. The images are available on website of U.S. National Library of Medicine. The proposed model uses various deep learning layers like convolution layer, max pooling layer, batch normalization layer and fully connected layer. The model achieves 99.71% accuracy in training and 98.23% accuracy on the test data. The study purposes a robust CNN models for detecting infected cell. The training and testing were performed on the 27,558 single cell images.
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Kumar, R., Singh, S.K., Khamparia, A. (2019). Malaria Detection Using Custom Convolutional Neural Network Model on Blood Smear Slide Images. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_3
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DOI: https://doi.org/10.1007/978-981-15-0108-1_3
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