Abstract
Malaria is a life-threating disease that affects millions of people, each year. The way of diagnosing malaria is visually examining blood smears under the microscope by skilled technicians for parasite-infected red blood cells. An automatic malaria classification based on machine learning can boost up the diagnosing process more effectively and efficiently to detect malaria in the earlier stage. In this research, an efficient and accurate convolutional neural network based model is proposed to classify malaria parasitic blood cell from microscope slides as whether infected or uninfected. The customized model trained on the NIH dataset consists of 27,578 RBC images, the accuracy of this model is 99.35% with 99.70% sensitivity and 99.00% specificity. The proposed model shows outform compare to others in terms of the performance indicators such as sensitivity, specificity, precision, F1 score, and Matthews’s correlation coefficient.
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Mondal, S.K., Islam, M., Faruque, M.O., Turja, M.S., Yusuf, M.S.U. (2023). Efficient Malaria Cell Image Classification Using Deep Convolutional Neural Network. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_34
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DOI: https://doi.org/10.1007/978-981-19-7528-8_34
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