409 research outputs found
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world
since it came in sight in December 2019. It has already caused thousands of
causalities and infected several millions worldwide. Any technological tool
that can be provided to healthcare practitioners to save time, effort, and
possibly lives has crucial importance. The main tools practitioners currently
use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction
(RT-PCR) and Computed Tomography (CT), which require significant time,
resources and acknowledged experts. X-ray imaging is a common and easily
accessible tool that has great potential for Covid-19 diagnosis. In this study,
we propose a novel approach for Covid-19 recognition from chest X-ray images.
Despite the importance of the problem, recent studies in this domain produced
not so satisfactory results due to the limited datasets available for training.
Recall that Deep Learning techniques can generally provide state-of-the-art
performance in many classification tasks when trained properly over large
datasets, such data scarcity can be a crucial obstacle when using them for
Covid-19 detection. Alternative approaches such as representation-based
classification (collaborative or sparse representation) might provide
satisfactory performance with limited size datasets, but they generally fall
short in performance or speed compared to Machine Learning methods. To address
this deficiency, Convolution Support Estimation Network (CSEN) has recently
been proposed as a bridge between model-based and Deep Learning approaches by
providing a non-iterative real-time mapping from query sample to ideally sparse
representation coefficient' support, which is critical information for class
decision in representation based techniques.Comment: 10 page
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
Convolutional Sparse Support Estimator Network (CSEN) : From Energy-Efficient Support Estimation to Learning-Aided Compressive Sensing
Support estimation (SE) of a sparse signal refers to finding the location indices of the nonzero elements in a sparse representation. Most of the traditional approaches dealing with SE problems are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery (SR) techniques to obtain support sets instead of directly mapping the nonzero locations from denser measurements (e.g., compressively sensed measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the convolutional sparse support estimator networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: 1) real-time and low-cost SE can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, and so on and 2) CSEN’s output can directly be used as “prior information,” which improves the performance of sparse SR algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity.publishedVersionPeer reviewe
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support
Estimator Networks (OSENs) for the support estimation task. Support Estimation
(SE) is defined as finding the locations of non-zero elements in a sparse
signal. By its very nature, the mapping between the measurement and sparse
signal is a non-linear operation. Traditional support estimators rely on
computationally expensive iterative signal recovery techniques to achieve such
non-linearity. Contrary to the convolution layers, the proposed OSEN approach
consists of operational layers that can learn such complex non-linearities
without the need for deep networks. In this way, the performance of the
non-iterative support estimation is greatly improved. Moreover, the operational
layers comprise so-called generative \textit{super neurons} with non-local
kernels. The kernel location for each neuron/feature map is optimized jointly
for the SE task during the training. We evaluate the OSENs in three different
applications: i. support estimation from Compressive Sensing (CS) measurements,
ii. representation-based classification, and iii. learning-aided CS
reconstruction where the output of OSENs is used as prior knowledge to the CS
algorithm for an enhanced reconstruction. Experimental results show that the
proposed approach achieves computational efficiency and outperforms competing
methods, especially at low measurement rates by a significant margin. The
software implementation is publicly shared at
https://github.com/meteahishali/OSEN
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
Restoration of poor quality images with a blended set of artifacts plays a
vital role for a reliable diagnosis. Existing studies have focused on specific
restoration problems such as image deblurring, denoising, and exposure
correction where there is usually a strong assumption on the artifact type and
severity. As a pioneer study in blind X-ray restoration, we propose a joint
model for generic image restoration and classification: Restore-to-Classify
Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model
keeps any disease intact after the restoration. Therefore, this will naturally
lead to a higher diagnosis performance thanks to the improved X-ray image
quality. To accomplish this crucial objective, we define the restoration task
as an Image-to-Image translation problem from poor quality having noisy,
blurry, or over/under-exposed images to high quality image domain. The proposed
R2C-GAN model is able to learn forward and inverse transforms between the two
domains using unpaired training samples. Simultaneously, the joint
classification preserves the disease label during restoration. Moreover, the
R2C-GANs are equipped with operational layers/neurons reducing the network
depth and further boosting both restoration and classification performances.
The proposed joint model is extensively evaluated over the QaTa-COV19 dataset
for Coronavirus Disease 2019 (COVID-19) classification. The proposed
restoration approach achieves over 90% F1-Score which is significantly higher
than the performance of any deep model. Moreover, in the qualitative analysis,
the restoration performance of R2C-GANs is approved by a group of medical
doctors. We share the software implementation at
https://github.com/meteahishali/R2C-GAN
Automatic detection of covid-19 based on CT Scan images using the convolution neural network
The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN model
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