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Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection (2202.10426v1)
Published 21 Feb 2022 in eess.IV, cs.CV, and cs.LG
Abstract: We apply convolutional neural networks to identify between malaria infected and non-infected segmented cells from the thin blood smear slide images. We optimize our model to find over 95% accuracy in malaria cell detection. We also apply Canny image processing to reduce training file size while maintaining comparable accuracy (~ 94%).
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