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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References
World Health Organization: World Malaria Report (2018)
Hisaeda, H., Yasutomo, K., Himeno, K.: Malaria: immune evasion by parasites. Int. J. Biochem. Cell Biol. 37(4), 700–706 (2005)
Global Technical Strategy for Malaria 2016–2030, WHO’S E-2020 initiative and malaria elimination
Di Ruberto, C., Dempster, A., Khan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image Vis. Comput. 20, 141–144 (2002)
Ross, N.E., Pritchard, C.J., Rubin, D.M., Duse, A.G.: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med. Biol. Eng. Comput. 44, 427–436 (2006)
Mitiku, K., Mengistu, G., Gelaw, B.: The reliability of blood film examination for malaria at The Peripheral health unit. Ethiop. J. Health Dev. 7, 97–204 (2003)
Pattanaik, P.A., Swarnkar, T., Sheet, D.: Object detection technique for malaria parasite in thin blood smear images. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2007)
Hendrawan, Y.F., Angkoso, C.V., Wahyuningrum, R.T.: Colour image segmentation for malaria parasites detection using cascading method. In: International Conference on SIET (2017)
Gopakumar, G., Swetha, M., Siva, G.S., Subrahmanyam, G.R.K.: CNN based malaria diagnosis from focus-stack of blood smear images acquired using custom-built slide scanner, Online Wiley Library (2018)
Dong, Y., et al.: Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. IEEE (2017)
Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)
Karpathy, A.: CS231n Convolutional Neural Networks for Visual Recognition (2018)
George, A., Routray, A.: Real-time eye gaze direction classification using convolutional neural network. In: International Conference on Signal Processing and Communications (SPCOM) (2016)
Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)
Liang, Z., et al.: CNN-based image analysis for malaria diagnosis. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Rajaraman, S., et al.: Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. PeerJ 6, e4568 (2018). https://doi.org/10.7717/peerj.4568
National Library of Medicine website, U.S. (2018). https://ceb.nlm.nih.gov/repositories/malaria-datasets/
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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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