Emergent Mind
Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection
(2202.10426)
Published Feb 21, 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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