Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection
Leading University（领先大学）；Clemson University（克莱姆森大学）
英文摘要：
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%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
