通过微调深度转移学习模型从胸部X射线图像对肺炎进行分类的基于合奏的方法

肺炎是由感染肺部的病毒，细菌或真菌引起的，如果不加以诊断，可能致命并导致呼吸衰竭。在美国，每年有25万以上的人（主要是成年人）被诊断出患有肺炎，其中有5万人死于该病。..

An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images

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.