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arXiv · 2202.10426

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

Tahsinur Rahman Talukdar·Mohammad Jaber Hossain·Tahmid H. Talukdar
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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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Tahsinur Rahman Talukdar, Mohammad Jaber Hossain, Tahmid H. Talukdar. 2022-02-21. Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection. https://arxiv.org/abs/2202.10426

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