An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images
This work addresses the need for improved diagnostic accuracy in pneumonia detection for medical practitioners, though it is incremental as it builds on existing deep learning methods.
The paper tackled pneumonia classification from chest X-ray images by fine-tuning and ensembling deep transfer learning models, achieving a test accuracy of 98.46%, precision of 98.38%, recall of 99.53%, and f1 score of 98.96%, which are reported as the highest levels in the literature.
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.