528 research outputs found
A Light CNN for detecting COVID-19 from CT scans of the chest
OVID-19 is a world-wide disease that has been declared as a pandemic by the
World Health Organization. Computer Tomography (CT) imaging of the chest seems
to be a valid diagnosis tool to detect COVID-19 promptly and to control the
spread of the disease. Deep Learning has been extensively used in medical
imaging and convolutional neural networks (CNNs) have been also used for
classification of CT images. We propose a light CNN design based on the model
of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with
other CT images (community-acquired pneumonia and/or healthy images). On the
tested datasets, the proposed modified SqueezeNet CNN achieved 83.00\% of
accuracy, 85.00\% of sensitivity, 81.00\% of specificity, 81.73\% of precision
and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot
without GPU acceleration). Besides performance, the average classification time
is very competitive with respect to more complex CNN designs, thus allowing its
usability also on medium power computers. In the next future we aim at
improving the performances of the method along two directions: 1) by increasing
the training dataset (as soon as other CT images will be available); 2) by
introducing an efficient pre-processing strategy
A Novel Approach to detect COVID-19 from chest X-ray images using CNN
In light of the present COVID-19 pandemic, it is important to consider the worth of human life, prosperity, and quality of life while also realizing that it is difficult to restrict case spread and mortality. One of the most difficult challenges for practitioners is identifying individuals who are COVID19-infected and isolating patients to stop COVID transmission. Therefore, identifying the covid19 infection is important. For the detection of COVID-19, a 4-6-hour reverse transcriptase chain reaction is used. Chest X-rays provide us with a different method for detecting Coronavirus early in the disease phase. We detected properties from chest X-ray scans and divided them into three categories with VGG16 as well as ResNet50 deep learning algorithms: COVID-19, normal, and viral pneumonia. To test the model's accuracy in specialized cases, we injected them with 15153 scans. The average COVID-19 case detection accuracy for the ResNet50 model is 91.39%, compared to 89.34% for the VGG16 model. However, a larger dataset is required when using deep learning to identify COVID-19. It accurately detects situations, which is the desired outcome
Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis
In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods
Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset
Computer tomography (CT) have been routinely used for the diagnosis of lung
diseases and recently, during the pandemic, for detecting the infectivity and
severity of COVID-19 disease. One of the major concerns in using ma-chine
learning (ML) approaches for automatic processing of CT scan images in clinical
setting is that these methods are trained on limited and biased sub-sets of
publicly available COVID-19 data. This has raised concerns regarding the
generalizability of these models on external datasets, not seen by the model
during training. To address some of these issues, in this work CT scan images
from confirmed COVID-19 data obtained from one of the largest public
repositories, COVIDx CT 2A were used for training and internal vali-dation of
machine learning models. For the external validation we generated
Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes
and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative
performance evaluation of four state-of-the-art machine learning models, viz.,
a lightweight convolutional neural network (CNN), and three other CNN based
deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in
classifying CT images into three classes, viz., normal, non-covid pneumonia,
and COVID-19 is carried out on these two datasets. Our analysis showed that the
performance of all the models is comparable on the hold-out COVIDx CT 2A test
set with 90% - 99% accuracies (96% for CNN), while on the external
Indian-COVID-19 CT dataset a drop in the performance is observed for all the
models (8% - 19%). The traditional ma-chine learning model, CNN performed the
best on the external dataset (accu-racy 88%) in comparison to the deep learning
models, indicating that a light-weight CNN is better generalizable on unseen
data. The data and code are made available at https://github.com/aleesuss/c19
Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images
Detecting COVID-19 patients using Computed Tomography (CT) images of the
lungs is an active area of research. Datasets of CT images from COVID-19
patients are becoming available. Deep learning (DL) solutions and in particular
Convolutional Neural Networks (CNN) have achieved impressive results for the
classification of COVID-19 CT images, but only when the training and testing
take place within the same dataset. Work on the cross-dataset problem is still
limited and the achieved results are low. Our work tackles the cross-dataset
problem through a Domain Adaptation (DA) technique with deep learning. Our
proposed solution, COVID19-DANet, is based on pre-trained CNN backbone for
feature extraction. For this task, we select the pre-trained Efficientnet-B3
CNN because it has achieved impressive classification accuracy in previous
work. The backbone CNN is followed by a prototypical layer which is a concept
borrowed from prototypical networks in few-shot learning (FSL). It computes a
cosine distance between given samples and the class prototypes and then
converts them to class probabilities using the Softmax function. To train the
COVID19-DANet model, we propose a combined loss function that is composed of
the standard cross-entropy loss for class discrimination and another entropy
loss computed over the unlabelled target set only. This so-called unlabelled
target entropy loss is minimized and maximized in an alternative fashion, to
reach the two objectives of class discrimination and domain invariance.
COVID19-DANet is tested under four cross-dataset scenarios using the
SARS-CoV-2-CT and COVID19-CT datasets and has achieved encouraging results
compared to recent work in the literature.Comment: 31 pages, 15 figure
Covid-19 Diagnosis Based on CT Images Through Deep Learning and Data Augmentation
Coronavirus disease 2019(Covid-19) has made people around the world suffer. And there are many researchers make efforts on deep learning methods based on CT imgaes, but the limitation of  this work is the lackage of the dataset, which is not easy to obtain. In this study, we try to use data augmentation to compensate this weakness. In the first part, we use traditional DenseNet-169, and the result shows that data augmentation can help improve the calculating speed and the accuracy. In the second part, we combine Self-trans and DenseNet-169, and the result shows that when doing data augmentation, many model performance metrics have been improved. In the third part, we use UNet++, which reaches accuracy of 0.8645. Apart from this, we think GAN and CNN may also make difference
Case Studies on X-Ray Imaging, MRI and Nuclear Imaging
The field of medical imaging is an essential aspect of the medical sciences,
involving various forms of radiation to capture images of the internal tissues
and organs of the body. These images provide vital information for clinical
diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and
nuclear imaging in detecting severe illnesses. However, manual evaluation and
storage of these images can be a challenging and time-consuming process. To
address this issue, artificial intelligence (AI)-based techniques, particularly
deep learning (DL), have become increasingly popular for systematic feature
extraction and classification from imaging modalities, thereby aiding doctors
in making rapid and accurate diagnoses. In this review study, we will focus on
how AI-based approaches, particularly the use of Convolutional Neural Networks
(CNN), can assist in disease detection through medical imaging technology. CNN
is a commonly used approach for image analysis due to its ability to extract
features from raw input images, and as such, will be the primary area of
discussion in this study. Therefore, we have considered CNN as our discussion
area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
Deep Learning Algorithms for Diagnosing Covid 19 Based on X-Ray and CT Images
An outbreak of a highly pathogenic coronavirus, which can cause chronic respiratory illness and high mortality rates. It takes a considerable amount of time to perform the polymerase chain reaction (PCR) used in COVID tests. Its accuracy ranges from 30% to 70%. In contrast, CT and chest X-ray diagnostics are 98% and 80% accurate in detecting COVID, respectively. A deep learning algorithms was applied to CT and X-ray images to enable rapid and accurately diagnosis of COVID-19 within seconds. In this survey, we revised all state-of-the-art studies of COVID-19 based on CT and X-ray images. Also, we analysed multiple deep learning networks and compared the performance of each technique. The result of the comparison shows that the baseline neural network has better efficiency in the recognition of COVID-19. The detection accuracy of baseline networks ranges between 93% and 98.7%. This shows the efficiency of deep learning techniques in identifying COVID-19
Transfer deep learning approach for detecting coronavirus disease in X-ray images
Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task
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