论文标题
使用神经网络在胸部X射线检查中检测肺炎
Pneumonia Detection in Chest X-Rays using Neural Networks
论文作者
论文摘要
随着AI的进步，深度学习技术被广泛用于在多个领域（例如医学诊断任务）设计出良好的性能的强大分类模型。在本文中，我们提出了CNN模型（卷积神经网络），以分类北美肺炎放射学会（RSNA）数据集的胸部X射线图像。这项研究还试图通过尝试对近年来实施的方法进行各种方法来使用有限的计算资源来实现相同的RSNA基准测试结果。提出的方法基于非复合CNN，并使用传递学习算法（例如Xception，Inceptionv3/v4，foriditedNetB7）。随之而来的是，该研究还试图通过使用有限的计算资源来实现相同的RSNA基准测试结果，通过尝试近年来实施的方法。 RSNA基准映射分数为0.25，但是在分层的3017样品中使用Mask RCNN模型以及图像增强得出的地图得分为0.15。同时，没有任何超参数调整的Yolov3的地图得分为0.32，但损失仍在减少。运行更多迭代的模型可以提供更好的结果。
With the advancement in AI, deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a non-complex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25, but using the Mask RCNN model on a stratified sample of 3017 along with image augmentation gave a MAP score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results.
