Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

By Ioannis D. Apostolopoulos, Tzani Bessiana. Physical and Engineering Sciences in Medicine, 2020

2,008 citations recorded by the source corpus.

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/790a5020f730491f6a2f35ef7a2bb8c18d42d963

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable goal, often with remarkable results [16].”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, methodology): “In [4], a dataset of X-ray images from patients with pneumonia, confirmed Covid-19 disease, and normal incidents, was used to evaluate the performance of state-of-the-art convolutional neural network architectures proposed previously for medical image classification.”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Apos-tolopoulos et al. [30] developed a deep learning model for the diagnosis of COVID-19.”
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2021, neutral): “There is a demand for medical image enhancement to help clinicians to make an accurate diagnosis of the diseases [6,8,12,32].”
COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, methodology): “…When classifying between COVID/non-COVID most recent works using this dataset have taken other datasets and treated them as non-COVID-19 [Wang and Wong, 2020; Apostolopoulos and Mpesiana, 2020] while results with balanced datasets from Tartaglione et al. [2020] report much lower performance.”
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images (2020, neutral): “Apostolopoulos and Mpesiana [16] evaluated various state-ofhe-art deep architectures on chest X-ray images.”
A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 (2020, neutral): “In another study, the results were presented in two forms, 2 and 3 classes, that due to the imbalance in the dataset, there are several meaningless results [39].”
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study (2020, background): “A similar attempt was recently performed on chest x-rays [4], which is a low-dose and less expensive test, with promising statistical performance (e.”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, methodology): “[43] for the same purpose as COVID-Net.”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, methodology): “[146] Chest X-rays TL + CNN TL is used for extracting patterns from common bacterial pneumonia patients X-rays using CNN to detect COVID-19.”
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization (2020, neutral): “[9] 224 COVID-19 + 700 Pneumonia + 504 Normal VGG-19 93.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, neutral): “…Transfer Learning 97.94 Farooq and Hafeez [65] Transfer Learning 96.20 Ucar and Korkmaz [41] Bayes-SqueezeNet 98.30 Apostolopoulos and Mpesiana [66] Transfer Learning 93.48 Xu, Jiang, Ma, Du, Li, Lv, Yu, Ni, Chen and Su [67] ResNet + Location Attention 86.70 Ozturk, Talo, Yildirim, Baloglu,…”
Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, neutral): “Apostolopoulos et al. [41] utilized TL approach on data-sets that contain 1427 X-ray images (504 normal X-ray images, 700 bacterial pneumonia, and 224 COVID-19 X-ray images).”
Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods (2020, result): “There are also some studies, which used lung X-ray scans [11-14].”