Performance assessment of Deep Learning procedures on Malaria dataset
DOI:
https://doi.org/10.18196/jrc.2145Keywords:
CNN, Computational Methods, ResNet, SequentialAbstract
Malaria detection is a time-consuming procedure. Only blood sample investigation is the practice which provides the confirmation. Now numerous computational methods have been used to make it faster. The proposed model uses the conception of Convolutional Neural Network (CNN) to lessen the time complexity in identification of Malaria. The prototypical model uses different deep learning algorithms which uses the same dataset to validate the stability. Model uses the two various components of CNN like Sequential and ResNet. ResNet uses more of number of hidden layers rather than sequential. The ResNet model achieved 96.50% accuracy on the training data, 96.78% accuracy on the validation data and 97% accuracy on the testing data. Sequential model on the other hand achieved 98% accuracy on the training data, 96% accuracy on the validation data and 96% accuracy on the testing data. From the initial hypothesis, we get to know that there is no significant difference in the accuracy when we have too many layers.
References
B. Maiseli, J. Mei, H. Gao, S. Yin and B. Maiseli, "An automaticand cost-effective parasitemia identification framework for low-end microscopy imaging devices," 2014 International Conference on Mechatronics and Control (ICMC), Jinzhou, 2014, pp. 2048-2053.
Chernin, Eli. “Sir Ronald Ross, Malaria, and the Rewards of Research.” Medical History 32, no 2 (1988), 119–41. doi:10.1017/S0025727300047967.
Bruno JM Feachem R Godal T et al. "The spirit of Dakar: a call for action on malaria." Nature. 1997; 386: 541K. Elissa, “Title of paper if known,”unpublished https://www.thelancet.com/commissions.
B. Shickel, P. J. Tighe, A. Bihorac and P. Rashidi, "Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis," in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1589-1604, Sept. 2018.
H. Xie, S. Wang, K. Liu, S. Lin and B. Hou, "Multilayer feature learning for polarimetric synthetic radar data classification," 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, 2014, pp. 2818-2821.
J. Angulo, G. Flandrin, “Automated detection of working area of peripheral blood smears using mathematical morphology”, U. S. National Library of Medicine, Analytical Cellular Pathology 25(1).
S. Lawrence, C. L. Giles, Ah Chung Tsoi and A. D. Back, "Face recognition: a convolutional neural-network approach," in IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98-113, Jan. 1997.
J. Latif, C. Xiao, A. Imran and S. Tu, "Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review," 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 2019, pp. 1-5.
A. Namozov and Y. I. Cho, "An Improvement for Medical Image Analysis Using Data Enhancement Techniques in Deep Learning," 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT), Busan, 2018, pp. 1-3.
J. Ker, L. Wang, J. Rao and T. Lim, "Deep Learning Applications in Medical Image Analysis," in IEEE Access, vol. 6, pp. 9375-9389, 2018.
Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie and S. Gjessing, "Cognitive machine-to-machine communications: visions and potentials for the smart grid," in IEEE Network, vol. 26, no. 3, pp. 6-13, May-June 2012.
Alkrimi, J.A., Toma, S.A., Mohammed, R.S. and Georged, L.E., 2020. COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES USING DATA MINING TO DETECT MALARIA-INFECTED RED BLOOD CELLS. International Journal of Advanced Research in Technology and Innovation, 1(3), pp.1-11.
S. Kapil, M. Chawla and M. D. Ansari, "On K-means data clustering algorithm with genetic algorithm," 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, 2016, pp. 202-206.
Kalkan, Soner Can, and Ozgur Koray Sahingoz. "Deep Learning Based Classification of Malaria from Slide Images." In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-4. IEEE, 2019.
Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude, RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. PeerJ6:e4568 https://doi.org/10.7717/peerj.4568
Lister Hill National Center for Biomedical Communications, National Library of Medicine, December 17, 1968.DOI: https://doi.org/10.1016/0002-9149(69)90022-8
Maude, R.J., Hasan, M.U., Hossain, M.A. et al. Temporal trends in severe malaria in Chittagong, Bangladesh. Malar J 11, 323 (2012). https://doi.org/10.1186/1475-2875-11-323
Direk Limmathurotsakul, Elizabeth L. Turner, Vanaporn Wuthiekanun, Janjira Thaipadungpanit, Yupin Suputtamongkol, Wirongrong Chierakul, Lee D. Smythe, Nicholas P. J. Day, Ben Cooper, Sharon J. Peacock Clinical Infectious Diseases, Volume 55 Issue 3,1 August 2012, Pages 322–331, https://doi.org/10.1093/cid/cis403
Q. Wang, Z. Teng, J. Xing, J. Gao, W. Hu and S. Maybank, "Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 4854-4863.
F. Zang and J. Zhang, "Softmax Discriminant Classifier," 2011 Third International Conference on Multimedia Information Networking and Security, Shanghai, 2011, pp. 16-19.
Yang, J.; Yang, G. Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer. Algorithms 2018, 11, 28.
H. Yang, C. Yuan, J. Xing and W. Hu, "SCNN: Sequential convolutional neural network for human action recognition in videos," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017, pp. 355-359.
K. Hara, D. Saito and H. Shouno, "Analysis of function of rectified linear unit used in deep learning," 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, 2015, pp. 1-8.
keras: R Interface to the Keras Deep Learning Library Taylor B Arnold1 DOI: 10.21105/joss.00296 1 University of Richmond, Department of Mathematics and Computer Science
https://healthitanalytics.com/features/what-is-deep-learning-and-how-will-it-change-healthcare
https://www.malariasite.com/malaria-india/
https://towardsdatascience.com/detecting-malaria-with-deep-learning-9e45c1e34b60
Murray, Naila, and Florent Perronnin. "Generalized max pooling." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
Ciregan, Dan, Ueli Meier, and Jürgen Schmidhuber. "Multi-column deep neural networks for image classification." 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012.
Inoue, Hiroshi. "Data augmentation by pairing samples for images classification." arXiv preprint arXiv:1801.02929 (2018).
https://www.who.int/malaria/publications/world-malaria-report-2017/en/
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This journal is based on the work at https://journal.umy.ac.id/index.php/jrc under license from Creative Commons Attribution-ShareAlike 4.0 International License. You are free to:
- Share – copy and redistribute the material in any medium or format.
- Adapt – remix, transform, and build upon the material for any purpose, even comercially.
The licensor cannot revoke these freedoms as long as you follow the license terms, which include the following:
- Attribution. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
• Creative Commons Attribution-ShareAlike (CC BY-SA)
JRC is licensed under an International License
