Background Deep learning using convolutional neural networks (CNN) has achieved significant results in various fields that use images. Deep learning can automatically extract features from data, and CNN extracts image features by convolution processing. We assumed that increasing the image size using interpolation methods would result in an effective feature extraction. To investigate how interpolation methods change as the number of data increases, we examined and compared the effectiveness of data augmentation by inversion or rotation with image augmentation by interpolation when the image data for training were small. Further, we clarified whether image augmentation by interpolation was useful for CNN training. To examine the usefulness of interpolation methods in medical images, we used a Gender01 data set, which is a sex classification data set, on chest radiographs. For comparison of image enlargement using an interpolation method with data augmentation by inversion and rotation, we examined the results of two- and four-fold enlargement using a Bilinear method. Results The average classification accuracy improved by expanding the image size using the interpolation method. The biggest improvement was noted when the number of training data was 100, and the average classification accuracy of the training model with the original data was 0.563. However, upon increasing the image size by four times using the interpolation method, the average classification accuracy significantly improved to 0.715. Compared with the data augmentation by inversion and rotation, the model trained using the Bilinear method showed an improvement in the average classification accuracy by 0.095 with 100 training data and 0.015 with 50,000 training data. Comparisons of the average classification accuracy of the chest X-ray images showed a stable and high-average classification accuracy using the interpolation method. Conclusion Training the CNN by increasing the image size using the interpolation method is a useful method. In the future, we aim to conduct additional verifications using various medical images to further clarify the reason why image size is important.

中文翻译：

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数据计数和图像缩放对深度学习训练的影响

背景使用卷积神经网络 (CNN) 的深度学习在使用图像的各个领域都取得了显著成果。深度学习可以自动从数据中提取特征，CNN通过卷积处理提取图像特征。我们假设使用插值方法增加图像大小将导致有效的特征提取。为了研究插值方法如何随着数据数量的增加而变化，我们检查并比较了当用于训练的图像数据较小时，通过反转或旋转进行数据增强与通过插值进行图像增强的有效性。此外，我们澄清了通过插值进行图像增强是否对 CNN 训练有用。为了检查医学图像中插值方法的有用性，我们使用了 Gender01 数据集，这是一个性别分类数据集，在胸片上。为了比较使用插值方法的图像放大与通过反转和旋转的数据增强，我们使用双线性方法检查了 2 倍和 4 倍放大的结果。结果通过使用插值法扩大图像尺寸，提高了平均分类精度。当训练数据数量为 100 时，改进最大，训练模型与原始数据的平均分类准确率为 0.563。然而，在使用插值方法将图像大小增加四倍后，平均分类精度显着提高到 0.715。与反演和旋转的数据增强相比，使用双线性方法训练的模型显示，在 100 个训练数据和 50,000 个训练数据的情况下，平均分类准确度提高了 0.095 和 0.015。胸部 X 线图像的平均分类精度比较表明，使用插值法可以获得稳定且高的平均分类精度。结论 通过使用插值方法增加图像大小来训练 CNN 是一种有用的方法。未来，我们的目标是使用各种医学图像进行额外的验证，以进一步阐明图像大小很重要的原因。结论 通过使用插值方法增加图像大小来训练 CNN 是一种有用的方法。未来，我们的目标是使用各种医学图像进行额外的验证，以进一步阐明图像大小很重要的原因。结论 通过使用插值方法增加图像大小来训练 CNN 是一种有用的方法。未来，我们的目标是使用各种医学图像进行额外的验证，以进一步阐明图像大小很重要的原因。