Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset

Abstract

One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).

Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model

“…CNN has demonstrated remarkable success in medical image classification tasks, such as identifying diseases based on medical images. For example, CNNs can accurately distinguish between normal and abnormal X-rays [12], tuberculosis [13], lung cancer [14], and covid-19 [15], and brain tumor detection [16]. In radiology, CNNs have been employed for accurate and efficient detection of abnormalities in mammograms [17], aiding in early breast cancer detection.…”