34 research outputs found
COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach
The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at the time of writing this paper (April 2020). Due to the number of contagious and deaths are continually growing day by day, the aim of this study is to develop a quick method to detect COVID-19 in chest X-ray images using deep learning techniques. For this purpose, an object detection architecture is proposed, trained and tested with a public available dataset composed with 1500 images of non-infected patients and infected with COVID-19 and pneumonia. The main goal of our method is to classify the patient status either negative or positive COVID-19 case. In our experiments using SDD300 model we achieve a 94.92% of sensibility and 92.00% of specificity in COVID-19 detection, demonstrating the usefulness application of deep learning models to classify COVID-19 in X-ray images
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating effect
on the global public health. Computed Tomography (CT) is an effective tool in
the screening of COVID-19. It is of great importance to rapidly and accurately
segment COVID-19 from CT to help diagnostic and patient monitoring. In this
paper, we propose a U-Net based segmentation network using attention mechanism.
As not all the features extracted from the encoders are useful for
segmentation, we propose to incorporate an attention mechanism including a
spatial and a channel attention, to a U-Net architecture to re-weight the
feature representation spatially and channel-wise to capture rich contextual
relationships for better feature representation. In addition, the focal tversky
loss is introduced to deal with small lesion segmentation. The experiment
results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices
are available, demonstrate the proposed method can achieve an accurate and
rapid segmentation on COVID-19 segmentation. The method takes only 0.29 second
to segment a single CT slice. The obtained Dice Score, Sensitivity and
Specificity are 83.1%, 86.7% and 99.3%, respectively.Comment: 14 pages, 6 figure