Malaria Parasite Detection Using Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47942-7_33
Reference11 articles.
1. Abubakar, A., Ajuji, M., Yahya, I.U.: DeepFMD: computational analysis for malaria detection in blood-smear images using deep-learning features. Appl. Syst. Innov. 4, 82 (2021). https://doi.org/10.3390/asi4040082
2. Masud, M., Alhumyani, H., Alshamrani, S.S., Cheikhrouhou, O., Ibrahim, S., Muhammad, G., Shamim Hossain, M., Shorfuzzaman, M.: Leveraging deep learning techniques for malaria parasite detection using mobile application. Wirel. Commun. Mob. Comput. 2020 (2020). https://doi.org/10.1155/2020/8895429. Article ID 8895429, 15 pages
3. Shekar, G., Revathy, S., Goud, E.K.: Malaria detection using deep learning. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 746–750 (2020). https://doi.org/10.1109/ICOEI48184.2020.9143023
4. Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), vol. 2017, pp. 464–472. https://doi.org/10.1109/WACV.2017.58
5. Dataset: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria
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