Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection
arXiv:2202.10426v12.75 citations
Originality Synthesis-oriented
AI Analysis
This work addresses malaria diagnosis for medical applications, but it is incremental as it combines existing methods.
The researchers tackled malaria detection from segmented blood cell images using convolutional neural networks, achieving over 95% accuracy, and applied Canny edge detection to reduce training file size while maintaining about 94% accuracy.
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%).