سال انتشار:

2021

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عنوان انگلیسی مقاله:

A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN

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ترجمه فارسی عنوان مقاله:

یک روش یادگیری عمیق برای غربالگری عفونت مالاریا: شمارش خودکار و سریع سلول ها ، تشخیص اشیاء و تقسیم بندی نمونه با استفاده از Mask R-CNN

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منبع:

Sciencedirect - Elsevier - Computerized Medical Imaging and Graphics, 88 (2021) 101845: doi:10:1016/j:compmedimag:2020:101845

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نویسنده:

De Rong Loh

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چکیده انگلیسی:

Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several com- puter vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost.
Keywords: Malaria diagnosis | Mask R-CNN | Computer vision | Image analysis

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قیمت: رایگان

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توضیحات اضافی:

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