CHEST X-RAY CLASSIFICATION BASED ON MACHINE LEARNING

Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest Xray dataset, containing over 100,000 frontalview X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases. Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper.