A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images

The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avoid its spreading increasingly. In this paper, a new CAD-based approach is suggested for the optimal diagnosis of this disease from chest X-ray images. The proposed method starts with a min-max normalization to scale all data into a normal scale, and then, histogram equalization is performed to improve the quality of the image before main processing. Afterward, 18 different features are extracted from the image. To decrease the method difficulty, the minimum features are selected based on a metaheuristic called Archimedes optimization algorithm (AOA). The model is then implemented on three datasets, and its results are compared with four other state-of-the-art methods. The final results indicated that the proposed method with 86% accuracy and 96% precision has the highest balance between accuracy and reliability with the compared methods as a diagnostic system for COVID-19.

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CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

10.1101/2021.05.19.21257430 ◽

2021 ◽

Author(s):

Md. Saikat Islam Khan ◽

Anichur Rahman ◽

Md. Razaul Karim ◽

Nasima Islam Bithi ◽

Shahab Band ◽

...

Keyword(s):

Healthcare Professionals ◽

Feature Fusion ◽

State Of The Art ◽

Research Community ◽

Diagnostic Model ◽

X Ray ◽

Multi Scale ◽

The World ◽

Chest X Ray ◽

Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

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Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1904-1909 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1904

Author(s):

Farah Flayeh Alkhalid ◽

Abdulhakeem Qusay Albayati ◽

Ahmed Ali Alhammad

Keyword(s):

Data Augmentation ◽

State Of The Art ◽

Virus Disease ◽

Histogram Equalization ◽

Vital Role ◽

X Ray ◽

Current State ◽

Proposed Model ◽

Chest X Ray ◽

Using Data

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

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COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

Diagnostics ◽

10.3390/diagnostics11020315 ◽

2021 ◽

Vol 11 (2) ◽

pp. 315

Author(s):

Soham Chattopadhyay ◽

Arijit Dey ◽

Pawan Kumar Singh ◽

Zong Woo Geem ◽

Ram Sarkar

Keyword(s):

State Of The Art ◽

Golden Ratio ◽

World Health ◽

Rt Pcr ◽

X Ray ◽

Global Pandemic ◽

The World ◽

Chest X Ray ◽

Feature Selection Approach ◽

Health Organization

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

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

Signal Image and Video Processing ◽

10.1007/s11760-020-01820-2 ◽

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.

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COVID-19 infection map generation and detection from chest X-ray images

Health Information Science and Systems ◽

10.1007/s13755-021-00146-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Aysen Degerli ◽

Mete Ahishali ◽

Mehmet Yamac ◽

Serkan Kiranyaz ◽

Muhammad E. H. Chowdhury ◽

...

Keyword(s):

State Of The Art ◽

Ground Truth ◽

Clinical Use ◽

X Ray ◽

Learning Techniques ◽

Map Generation ◽

Severity Grading ◽

Chest X Ray ◽

Novel Method ◽

Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

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Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

Journal of Neonatology ◽

10.1177/09732179211023763 ◽

2021 ◽

Vol 35 (2) ◽

pp. 93-94

Author(s):

Jyotsna Bhushan ◽

Shagufta Iqbal ◽

Abhishek Chopra

Keyword(s):

Respiratory Distress ◽

Conservative Management ◽

Preterm Neonate ◽

Late Preterm ◽

Intravenous Fluids ◽

X Rays ◽

Blood Gas ◽

X Ray ◽

Spontaneous Pneumomediastinum ◽

Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

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Acute Mitral Regurgitation: an important cause of respiratory distress

Acute Medicine Journal ◽

10.52964/amja.0598 ◽

2016 ◽

Vol 15 (1) ◽

pp. 30-32

Author(s):

Legate Philip ◽

◽

Neil Andrews ◽

Keyword(s):

Mitral Valve ◽

Mitral Regurgitation ◽

Respiratory Distress ◽

Severe Mitral Regurgitation ◽

Shortness Of Breath ◽

Valve Repair ◽

X Ray ◽

Clinical Presentations ◽

Acute Mitral Regurgitation ◽

Chest X Ray

Acute mitral regurgitation (acute MR) is a rare cause of acute respiratory distress, which can present diagnostic challenges. We present the case of a 57 year old man who developed acute shortness of breath subsequently associated with fever, raised white cells and elevated CRP. Chest x-ray revealed unilateral shadowing and he was treated for pneumonia, despite the finding of severe mitral regurgitation on echo. Failure to respond to antibiotic treatment following 3 weeks on ITU led to the consideration of acute MR as the cause of his symptoms and he responded well to diuretics. He subsequently underwent mitral valve repair. The causes and clinical presentations of this condition are discussed.

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Correlation between chest x-ray findings and outcomes of patients with mechanical ventilation

Paediatrica Indonesiana ◽

10.14238/pi53.1.2013.6-11 ◽

2013 ◽

Vol 53 (1) ◽

pp. 6

Author(s):

Indah Nurhayati ◽

Muhammad Supriatna ◽

Kamilah Budhi Raharjani ◽

Eddy Sudijanto

Keyword(s):

Mechanical Ventilation ◽

Retrospective Study ◽

Pleural Effusion ◽

Respiratory Distress ◽

Pulmonary Edema ◽

Patient Outcomes ◽

Morbidity And Mortality ◽

Chi Square ◽

X Ray ◽

Chest X Ray

Background Most infants and children admitted to the pediatricintensive care unit (PICU) have respiratory distress and pulmonarydisease as underlying conditions. Mechanical ventilation may beused to limit morbidity and mortality in children with respiratoryfailure.Objective To assess a correlation between chest x-ray findingsand outcomes of patients with mechanical ventilation.Methods This retrospective study was held in Dr. KariadiHospital, Semarang, Indonesia. Data was collected from themedical records of children admitted to the PICU from Januaryto December 2010, who suffered from respiratory distress andused mechanical ventilation. We compared chest x-ray findings tothe outcomes of patients. Radiological expertise was provided byradiologists on duty at the time. Chi-square and logistic regressiontests were used for statistical analysis.Results There were 63 subjects in our study, consisting of 28 malesand 35 females. Patient outcomes were defined as survived or died,43 subjects ( 68%) and 20 subjects (3 2%), respectively. Chest x-rayfindings revealed the following conditions: bronchopneumonia48% (P=0.298; 95%CI 0.22 to 1.88), pleural effusion 43%(P=0.280; 95%CI 0.539 to 4.837) , pulmonary edema 6%(P=0.622; 95%CI 0.14 to 14.62) and atelectasis 3% (P=0.538;95%CI 0.03 to 7 .62). None of the chest x-ray findings significantlycorrelated to patient outcomes.Conclusion Chest x-ray findings do not correlate to patientoutcomes in pediatric subjects with mechanical ventilation inthe PICU of Dr. Kariadi Hospital, Semarang, Indonesia.

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Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽

10.3390/s20226450 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6450

Author(s):

Taimur Hassan ◽

Muhammad Shafay ◽

Samet Akçay ◽

Salman Khan ◽

Mohammed Bennamoun ◽

...

Keyword(s):

Transfer Learning ◽

State Of The Art ◽

Autonomous Systems ◽

Aviation Security ◽

Dual Energy ◽

Average Precision ◽

X Ray ◽

Shot Classification ◽

Object Proposals ◽

The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

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COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm

BioMed Research International ◽

10.1155/2021/2295920 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ali Farki ◽

Zahra Salekshahrezaee ◽

Arash Mohammadi Tofigh ◽

Reza Ghanavati ◽

Behdad Arandian ◽

...

Keyword(s):

State Of The Art ◽

Optimal Method ◽

X Ray ◽

Suggested Technique ◽

Improve Accuracy ◽

Sensitivity Rate ◽

Chest X Ray ◽

Therapeutic Measures ◽

Simulation Results ◽

Day By Day

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others.

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