mlsanityData to 14 Oct 2026

Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection

arXiv 2202.10426 ↗First posted 21 Feb 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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%).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 21 Feb 2022.

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References · 4

  1. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  2. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 221
  3. 2017Towards Accurate Binary Convolutional Neural Networkcited by 149
  4. 2020Convolutional Networks with Dense Connectivitycited by 98

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