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N-Crop Based Image Division in Deep Learning with Medical Image

机译:基于N作物的医学图像深度学习中的图像分割

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摘要

In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.
机译:在本文中,我们提出了一种图像分割技术,该技术可以解决由于模型结构和医学图像的特性引起的数据不足而导致的分辨率降低的问题。为了验证该技术,我们比较了传统全图像学习和分割图像学习的性能。结果,证实了图像分割技术可以更稳定地进行X射线图像深度学习,并且在以更高的准确度预测结核病检测方面有效。

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