Abstract
This work describes an automatic system for malaria detection. Red blood corpuscles infected with malaria parasites of Giemsa stained segmented cells of thin-blood smeared slides are taken as input images. Initially, image processing techniques such as image resizing and bilateral filtering technique for noise removal, are applied. Further, deep learning-based convolution neural layer network models are proposed for malaria detection. Additionally, alongside comparison with other approaches and methodologies, comparison of various traditional machine learning algorithms is also done. Results show that the proposed model demonstrated in this work performs the best on the given input images with the highest accuracy of 95%, specificity score of 93.2% and sensitivity score of 96.8%.
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Kumar, K., Chandiramani, G., Kashyap, K.L. (2021). Computer-Aided Malaria Detection Based on Computer Vision and Deep Learning Approach. In: Bajpai, M.K., Kumar Singh, K., Giakos, G. (eds) Machine Vision and Augmented Intelligence—Theory and Applications. Lecture Notes in Electrical Engineering, vol 796. Springer, Singapore. https://doi.org/10.1007/978-981-16-5078-9_44
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