Applied Sciences (Computer Applications), I.K. Gujral Punjab Technical University, Jalandhar, Kapurthala, India.
Department of Computer Science and Engineering, Khalsa College of Engineering and Technology, Amritsar, India.
Med Biol Eng Comput. 2024 Oct;62(10):3123-3150. doi: 10.1007/s11517-024-03127-7. Epub 2024 May 24.
Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The augmentation approaches must enhance the model's performance during the learning period. There are several types of transformations that can be applied to medical images. These transformations can be applied to the entire dataset or to a subset of the data, depending on the desired outcome. In this study, we categorize data augmentation methods into four groups: Absent augmentation, where no modifications are made; basic augmentation, which includes brightness and contrast adjustments; intermediate augmentation, encompassing a wider array of transformations like rotation, flipping, and shifting in addition to brightness and contrast adjustments; and advanced augmentation, where all transformation layers are employed. We plan to conduct a comprehensive analysis to determine which group performs best when applied to brain CT images. This evaluation aims to identify the augmentation group that produces the most favorable results in terms of improving model accuracy, minimizing diagnostic errors, and ensuring the robustness of the model in the context of brain CT image analysis.
深度学习（DL）需要大量的训练数据来提高性能和防止过拟合。为了克服这些困难，我们需要增加训练数据集的规模。这可以通过在小数据集上进行扩充来实现。扩充方法必须在学习期间提高模型的性能。有几种类型的变换可以应用于医学图像。这些变换可以应用于整个数据集，也可以应用于数据的子集，具体取决于所需的结果。在这项研究中，我们将数据扩充方法分为四组：无扩充，即不进行任何修改；基本扩充，包括亮度和对比度调整；中级扩充，除了亮度和对比度调整外，还包括旋转、翻转和移位等更广泛的变换；高级扩充，其中使用所有变换层。我们计划进行全面分析，以确定在应用于脑 CT 图像时，哪一组表现最佳。该评估旨在确定在提高模型准确性、最小化诊断错误以及确保模型在脑 CT 图像分析方面的稳健性方面，哪个扩充组产生的结果最佳。
