Pneumonia and COVID-19 Detection in Chest X-rays Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN)

• Published in: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) pp. 01 - 08
• Main Authors: Farhat, Hanan J., Sakr, George E., Kilany, Rima
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.09.2022
• Subjects: Artificial intelligence; Chest X-rays; Computed tomography; Convolutional neural networks; COVID-19; Deep Learning; Distance measurement; Faster R-CNN; Medical Imaging; Polymers; Pulmonary diseases
• ISSN: 2641-3604
• DOI: 10.1109/BHI56158.2022.9926872

The arising of SARS-CoV-2 or 2019 novel coron-avirus in December 2019 have prioritized research on pulmonary diseases diagnosis and prognosis, especially using artificial intelligence (AI) and Deep Learning (DL). Polymerase Chain Reaction (PCR) is the most widely used technique to detect SARS-CoV-2, with a 0.12% false negative rate. While 75% of the hospitalized cases develop pneumonia caused by the virus, patients can still develop bacterial pneumonia. COVID-19 pneumonia can be diagnosed based on clinical data and Computed Tomography (CT scan). However, Chest X-rays are faster, cheaper, emit less radiations, and can be performed on bed-side. In this article, we extend the application of VGG-16 based Faster Region-Based Convolutional Neural Network (Faster R-CNN) to the detection of Pneumonia and COVID-19 in Chest X-ray images using several public datasets of total images count ranging from 2122 to 18455 Chest X-rays, and study the impact of several hyper-parameters such as objectness threshold and epochs count and length, to optimize the model's performance. Our results comply with the state of the art of Faster R-CNN in pneumonia detection as the best accuracy achieved is 65%. For COVID-19 detection, Faster R-CNN achieves a 90% validation accuracy.