论文标题
在胸部X射线图像中检测疾病检测的转移学习感知神经进化
Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images
论文作者
论文摘要
由于在图像上训练时，神经网络需要过多的时间成本。转移学习和微调可以帮助提高训练神经网络时的时间和成本效率。但是，转移学习和微调需要大量的实验。因此，需要一种找到用于转移学习和微调的最佳体系结构的方法。为了克服这个问题，可以使用遗传算法的神经进化来找到转移学习的最佳体系结构。为了检查这项研究的性能，使用了数据集ChestX射线14和Densenet-121作为基础神经网络模型。这项研究使用了AUC得分，训练执行时间的差异以及McNemar的测试进行了显着性测试。在结果方面，这项研究的AUC分数差异为5％，在执行时间方面快3％，并且在大多数疾病检测中的意义。最后，这项研究给出了一个具体的摘要，内容涉及神经进化转移学习如何在转移学习和微调方面有助于。
The neural network needs excessive costs of time because of the complexity of architecture when trained on images. Transfer learning and fine-tuning can help improve time and cost efficiency when training a neural network. Yet, Transfer learning and fine-tuning needs a lot of experiment to try with. Therefore, a method to find the best architecture for transfer learning and fine-tuning is needed. To overcome this problem, neuro-evolution using a genetic algorithm can be used to find the best architecture for transfer learning. To check the performance of this study, dataset ChestX-Ray 14 and DenseNet-121 as a base neural network model are used. This study used the AUC score, differences in execution time for training, and McNemar's test to the significance test. In terms of result, this study got a 5% difference in the AUC score, 3 % faster in terms of execution time, and significance in most of the disease detection. Finally, this study gives a concrete summary of how neuro-evolution transfer learning can help in terms of transfer learning and fine-tuning.
