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

机译:基于N-Troam基于医学图像的图像分裂

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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划分技术,可以解决由于模型结构和由医学图像特征引起的数据而导致的分辨率降低的问题。为了验证这种技术,我们比较了传统的完整图像学习和分割图像学习的性能。结果,确认图像分裂技术可以在X射线图像深度学习中更稳定地进行,并且具有更高的准确性预测结核病检测。

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