2,351 research outputs found
Smart COVID-3D-SCNN: A novel method to classify x-ray images of COVID-19
The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia)
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Coronavirus disease 2019 (COVID-19) has rapidly become a global health
concern after its first known detection in December 2019. As a result, accurate
and reliable advance warning system for the early diagnosis of COVID-19 has now
become a priority. The detection of COVID-19 in early stages is not a
straightforward task from chest X-ray images according to expert medical
doctors because the traces of the infection are visible only when the disease
has progressed to a moderate or severe stage. In this study, our first aim is
to evaluate the ability of recent \textit{state-of-the-art} Machine Learning
techniques for the early detection of COVID-19 from chest X-ray images. Both
compact classifiers and deep learning approaches are considered in this study.
Furthermore, we propose a recent compact classifier, Convolutional Support
Estimator Network (CSEN) approach for this purpose since it is well-suited for
a scarce-data classification task. Finally, this study introduces a new
benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage
COVID-19 pneumonia samples (very limited or no infection signs) labelled by the
medical doctors and 12 544 samples for control (normal) class. A detailed set
of experiments shows that the CSEN achieves the top (over 97%) sensitivity with
over 95.5% specificity. Moreover, DenseNet-121 network produces the leading
performance among other deep networks with 95% sensitivity and 99.74%
specificity.Comment: 12 page
Can AI help in screening Viral and COVID-19 pneumonia?
Coronavirus disease (COVID-19) is a pandemic disease, which has already
caused thousands of causalities and infected several millions of people
worldwide. Any technological tool enabling rapid screening of the COVID-19
infection with high accuracy can be crucially helpful to healthcare
professionals. The main clinical tool currently in use for the diagnosis of
COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which
is expensive, less-sensitive and requires specialized medical personnel. X-ray
imaging is an easily accessible tool that can be an excellent alternative in
the COVID-19 diagnosis. This research was taken to investigate the utility of
artificial intelligence (AI) in the rapid and accurate detection of COVID-19
from chest X-ray images. The aim of this paper is to propose a robust technique
for automatic detection of COVID-19 pneumonia from digital chest X-ray images
applying pre-trained deep-learning algorithms while maximizing the detection
accuracy. A public database was created by the authors combining several public
databases and also by collecting images from recently published articles. The
database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579
normal chest X-ray images. Transfer learning technique was used with the help
of image augmentation to train and validate several pre-trained deep
Convolutional Neural Networks (CNNs). The networks were trained to classify two
different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and
COVID-19 pneumonia with and without image augmentation. The classification
accuracy, precision, sensitivity, and specificity for both the schemes were
99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%,
respectively.Comment: 12 pages, 9 Figure
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