论文标题
通过微调深度转移学习模型来对胸部X射线图像进行分类，一种基于合奏的方法
An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images
论文作者
论文摘要
肺炎是由感染肺部的病毒，细菌或真菌引起的，如果未被诊断出，可能是致命的并导致呼吸衰竭。每年，美国主要是25万人，主要是成年人，患有肺炎，50,000人死于这种疾病。放射科医生广泛使用了胸部射线照相（X射线）来检测肺炎。忽略训练有素的放射科医生的肺炎检测并不少见，这触发了诊断准确性的提高的需求。在这项工作中，我们建议使用转移学习，这可以减少神经网络的训练时间并最大程度地减少泛化错误。我们培训了最先进的深度学习模型，例如InceptionResnet，Mobilenetv2，Xception，Densenet201和Resnet152v2，以准确地对肺炎进行分类。后来，我们创建了这些模型的加权平均合奏，并达到了98.46％的测试准确性，精度为98.38％，召回99.53％，F1得分为98.96％。这些精度，精度和F1评分的性能指标是文献中报道的最高水平，可以将其视为准确的肺炎分类的基准。
Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely used by radiologists to detect pneumonia. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. In this work, we propose using transfer learning, which can reduce the neural network's training time and minimize the generalization error. We trained, fine-tuned the state-of-the-art deep learning models such as InceptionResNet, MobileNetV2, Xception, DenseNet201, and ResNet152V2 to classify pneumonia accurately. Later, we created a weighted average ensemble of these models and achieved a test accuracy of 98.46%, precision of 98.38%, recall of 99.53%, and f1 score of 98.96%. These performance metrics of accuracy, precision, and f1 score are at their highest levels ever reported in the literature, which can be considered a benchmark for the accurate pneumonia classification.
