scholarly journals KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN

2021 ◽  
Vol 7 ◽  
pp. e694
Author(s):  
Mundher Mohammed Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali ◽  
Mohammed Alghaili ◽  
Asaad Shakir Hameed ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
X Ray ◽  
Infected People ◽  
Kl Divergence ◽  
Novel Approach ◽  
Starting Point ◽  
The World ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.

scholarly journals KL-MOB Automated Covid-19 Recognition Using a Novel Approach Based on Image Enhancement and a Modified MobileNet CNN

2021 ◽  
Author(s):  
Mundher Mohammed Taresh ◽  
Ning bo Zhu ◽  
Asaad Shakir Hameed ◽  
Modhi Lafta Mutar ◽  
Talal Ahmed Ali Ali Ahmed Ali Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
X Ray ◽  
Kl Divergence ◽  
Fine Grained ◽  
Novel Approach ◽  
Starting Point ◽  
The World ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

The emergence of the novel coronavirus pneumonia (Covid-19) pandemic at the end of 2019 led to chaos worldwide. The world breathed a sigh of relief when some countries announced that they had obtained the appropriate vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this disease has returned us to the starting point. At present, early detection of infected cases has been the paramount concern of both specialists and health researchers. This paper aims to detect infected patients through chest X-ray images. The large dataset available online for Covid-19 (COVIDx) was used in this research. The dataset consists of 2,128 x-ray images of Covid-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm was applied to improve image quality before conducting the neural network training process. This algorithm consisted of combining two different noise reduction filters in the images, followed by a contrast enhancement algorithm. In this paper, for Covid-19 detection, a novel convolution neural network (CNN) architecture, KL-MOB (Covid-19 detection network based on MobileNet structure), was proposed. KL-MOB performance was boosted by adding the Kullback Leibler (KL) divergence loss function at the end when trained from scratch. The Kullback-Leibler (KL) divergence loss function was adopted as content-based image retrieval and fine-grained classification to improve the quality of image representation. This paper yielded impressive results, overall benchmark accuracy, sensitivity, specificity, and precision of 98.7%, 98.32%, 98.82%, and 98.37%, respectively. The promising results in this research may enable other researchers to develop modern and innovative methods to aid specialists. The tremendous potential of the method proposed in this research can also be utilized to detect Covid-19 quickly and safely in patients throughout the world.

scholarly journals Performance Result for Detection of COVID-19 using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 699-703
Keyword(s):  
Neural Network ◽  
Classification Model ◽  
Classification Models ◽  
X Ray ◽  
The World ◽  
Rectified Linear Unit ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
The Masses ◽  
Fully Connected

The 2019 novel coronavirus (COVID-19), which has sprawled fleetly among masses residing in distant nations, had a prefatory juncture in China. From both a safeness and a lucrative outlook, it has staggered the world with its hasty diffusion with conjectural vicious generic repercussions for the masses. Consequent to the escalating cases daily, there is a constricted fraction of COVID-19 inspection kits acquirable in healthcare institutions. Ergo, to obviate COVID-19 propagating betwixt masses, it is imperative to enforce an instinctive unveiling network as a prompt jack legging diagnosis appendage. The contemplated method embroils a convolutional neural network- based model, namely ResNet50, concerted with a Fully Connected Layer (FCL), reinforced by Rectified Linear Unit (ReLU) for the unearthing of coronavirus pneumonia imparted sufferer by harnessing chest X-ray radiographs. The endorsed classification model, i.e. resnet50 affirmed by FCL and ReLU, compassed accuracy of 94% for unearthing COVID-19. When equated to diverse classification models, the purported model is preeminent. The aftereffect is premised on the attested X-ray images from the data appropriable in the arsenal of Kaggle

scholarly journals Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images

2021 ◽  
Author(s):  
Kamal KC ◽  
Zhendong Yin ◽  
Mingyang Wu ◽  
Zhilu Wu
Keyword(s):  
Neural Network ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
Test Accuracy ◽  
Computationally Efficient ◽  
X Ray ◽  
The World ◽  
Data Problem ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractThe COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

scholarly journals Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 548
Author(s):  
Mateus Maia ◽  
Jonatha S. Pimentel ◽  
Ivalbert S. Pereira ◽  
João Gondim ◽  
Marcos E. Barreto ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Predictive Performance ◽  
Polynomial Kernel ◽  
Support Vector ◽  
X Rays ◽  
X Ray ◽  
Applied Study ◽  
The World ◽  
Simulated Images

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.

scholarly journals IVAN PULUJ AND THE DISCOVERY OF X-RAYS

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Ulyana Pidvalna ◽  
◽  
Roman Plyatsko ◽  
Vassyl Lonchyna ◽  
◽  
...  
Keyword(s):  
International Exhibition ◽  
Bone Lesions ◽  
X Rays ◽  
High Quality ◽  
X Ray ◽  
Exact Date ◽  
Starting Point ◽  
The World ◽  
New Type ◽  
The Given

On January 5, 1896, the Austrian newspaper Die Presse published an article entitled “A Sensational Discovery”. It was dedicated to the discovery of X-rays made on November 8, 1895 by the German physicist Wilhelm Conrad Röntgen. Having taken into account the contribution of other scientists, the precondition of the given epochal, yet unexpected, discovery was, first and foremost, the work of the Ukrainian scientist Ivan Puluj. It was Puluj who laid the foundation for X-ray science. He explained the nature of X-rays, discovered that they can ionize atoms and molecules, and defined the place of X-ray emergence and their distribution in space. In 1881, Puluj constructed a cathode lamp (“Puluj’s tube”) which was fundamentally a new type of light source. In the same year, in recognition of this discovery, Puluj received an award at the International Exhibition in Paris. Investigating the processes in cathode-ray tubes, Ivan Puluj set the stage for two ground-breaking discoveries in physics, namely X-rays and electrons. Puluj used his cathode lamp in medicine as a source of intense X-rays which proved to be highly efficient. The exact date of the first X-ray images received by Puluj remains unknown. High-quality photographs of the hand of an eleven-year-old girl, taken on January 18, 1896, are preserved. Multiple X-ray images clearly visualized pathological changes in the examined structures (fractures, calluses, tuberculous bone lesions). High-quality images were obtained by means of the anticathode in the design of Puluj’s lamp, which was the first in the world. The image of the whole skeleton of a stillborn child (published on April 3, 1896 in The Photogram) is considered to be the starting point of using X-rays in anatomy.

scholarly journals Analysis and Prediction for the Spreading of Covid-19 Pandemic in India Using Mathematical Modeling

2020 ◽  
Author(s):  
Nanda Poddar ◽  
Subham Dhar ◽  
Kajal Kumar Mondal ◽  
Gourab Saha
Keyword(s):  
Mathematical Modeling ◽  
Applied Mathematics ◽  
Medical Science ◽  
Spreading Rate ◽  
Living Room ◽  
Daily Lives ◽  
Infected People ◽  
Effective Strategies ◽  
The World ◽  
Novel Coronavirus

In the present time, the biggest problem of the world is the outbreak of novel coronavirus. Novel coronavirus (COVID-19), this one name has become a part of our daily lives over the past few months. Beyond the boundaries of medical science, coronavirus is now the main subject of research in all fields like Applied Mathematics, Economy, Philosophy, Sociology, Politics upto living room. The epidemic has brought unimaginable changes in our traditional habits and daily routines. Thousands of people in our country are fighting with the rest of the world to survive in various new situations. There are different kinds of coronavirus appeared in different times. In this time, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the coronavirus disease of 2019 (COVID-19). This virus was first identified towards the end of 2019 in the city of Wuhan in the province of Hubei in China. Within very short duration of time and very fast, it has spread throughout a large part of the world. In this study, the main aim is to investigate the spreading rate, death rate, recovery rate due to corona virus infection and to study the future of the coronavirus in India by using mathematical modeling based on the previous data. Mathematical models, in this situation, are the important tools in recruiting effective strategies to fight this epidemic. India is at high risk of spreading the disease and is facing many losses in socio-economic aspects. With current infection rates and existing levels of personal alertness, the number of infected people in India will increase at least in the next three months. Proper social awareness, maintain of social distance, large rate of testing and separation may break the chain of the Coronavirus-2.

scholarly journals A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tahia Tazin ◽  
Sraboni Sarker ◽  
Punit Gupta ◽  
Fozayel Ibn Ayaz ◽  
Sumaia Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Learning Strategy ◽  
Severe Form ◽  
Ultrasound Images ◽  
X Ray ◽  
Novel Approach ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Feature Extraction

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients’ quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

scholarly journals Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Amin Alqudah
Keyword(s):  
Early Diagnosis ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Softmax Classifier ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

Abstract Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

scholarly journals A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

2021 ◽  
Vol 11 (24) ◽  
pp. 11902
Author(s):  
Sonain Jamil ◽  
MuhibUr Rahman
Keyword(s):  
Feature Vector ◽  
Classification Error ◽  
X Ray ◽  
Learning Framework ◽  
Spatial Features ◽  
Proposed Model ◽  
Chest X Ray ◽  
Dual Stage ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

Novel coronavirus, known as COVID-19, is a very dangerous virus. Initially detected in China, it has since spread all over the world causing many deaths. There are several variants of COVID-19, which have been categorized into two major groups. These groups are variants of concern and variants of interest. Variants of concern are more dangerous, and there is a need to develop a system that can detect and classify COVID-19 and its variants without touching an infected person. In this paper, we propose a dual-stage-based deep learning framework to detect and classify COVID-19 and its variants. CT scans and chest X-ray images are used. Initially, the detection is done through a convolutional neural network, and then spatial features are extracted with deep convolutional models, while handcrafted features are extracted from several handcrafted descriptors. Both spatial and handcrafted features are combined to make a feature vector. This feature vector is called the vocabulary of features (VoF), as it contains spatial and handcrafted features. This feature vector is fed as an input to the classifier to classify different variants. The proposed model is evaluated based on accuracy, F1-score, specificity, sensitivity, specificity, Cohen’s kappa, and classification error. The experimental results show that the proposed method outperforms all the existing state-of-the-art methods.

scholarly journals Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

2020 ◽  
Author(s):  
Hamed Jelodar ◽  
Yongli Wang ◽  
Rita Orji ◽  
Hucheng Huang
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Recurrent Neural Network ◽  
Online Discussions ◽  
Sentiment Classification ◽  
World Health ◽  
Computational Techniques ◽  
Public Opinions ◽  
The World ◽  
Novel Coronavirus

AbstractInternet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19–related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.

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