Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration

The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE's sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

An empirical study on vulnerability assessment and penetration detection for highly sensitive networks

With the advancement of internet and the emergence of network globalization, security has always been a major concern. During the trial operation, the management control platform discussed in this article included more than 600 network security vulnerabilities in the industry, with dozens of incidents, which were promptly dealt with and rectified, effectively improving the level of network security management and protection in the industry. As networks are very much vulnerable to denial of service attacks, much more emphasis has been given to security. By improving their network security, network administrators have often tried their best. To attempt penetration testing, it is the best way of ensuring the system security. With the development of information technology, the security requirement of information system is increasing day by day. The use of penetration testing technology is conducive to the realization of accurate positioning, accurate detection, and active alarm of security vulnerabilities, and the optimization of monitoring and rectification of the combination of network security management control system. Taking penetration testing technology as one of the core elements of management and control, the risk index model is optimized to make network security management controllable and efficient, and effectively achieve management and control objectives.

A Novel Hybrid Precoding-Companding Technique for Peak-to-Average Power Ratio Reduction in 5G and beyond

Several high-speed wireless systems use Orthogonal Frequency Division Multiplexing (OFDM) due to its advantages. 5G has adopted OFDM and is expected to be considered beyond 5G (B5G). Meanwhile, OFDM has a high Peak-to-Average Power Ratio (PAPR) problem. Hybridization between two PAPR reduction techniques gains the two techniques’ advantages. Hybrid precoding-companding techniques are attractive as they require small computational complexity to achieve high PAPR reduction gain. Many precoding-companding techniques were introduced to increasing the PAPR reduction gain. However, reducing Bit Error Rate (BER) and out-of-band (OOB) radiation are more significant than increasing PAPR reduction gain. This paper proposes a new precoding-companding technique to better reduce the BER and OOB radiation than previous precoding-companding techniques. Results showed that the proposed technique outperforms all previous precoding-companding techniques in BER enhancement and OOB radiation reduction. The proposed technique reduces the Error Vector Magnitude (EVM) by 15 dB compared with 10 dB for the best previous technique. Additionally, the proposed technique increases high power amplifier efficiency (HPA) by 11.4%, while the best previous technique increased HPA efficiency by 9.8%. Moreover, our proposal achieves PAPR reduction gain better than the most known powerful PAPR reduction technique with a 99% reduction in required computational complexity.

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective

COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this article, an optimization problem is formulated that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we solve the formulated problem by applying a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Furthermore, the sustainability of the lockdown policy is interpreted with the help of our proposed game-theoretic incentive model for maintaining social distancing where there exists a Nash equilibrium. Finally, we perform an extensive numerical analysis that shows the effectiveness of the proposed approach in terms of achieving the desired social-distancing to prevent the outbreak of the COVID-19 in a noncooperative environment. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

Convolutional neural network-based models for diagnosis of breast cancer

Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models’ training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

Kouzani A, Mahmud MAP. A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

Mitigation of Distributed Denial of Service Attacks in the Cloud

Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.

Role of Oral Clonidine Premedication on Intra-operative Haemodynamics and PONV in Laparoscopic Cholecystectomy

Laparoscopic cholecystectomy is a gold standard surgical procedure for gallbladder operation. It causes altered haemodynamic responses due to pneumoperitoneum and surgical procedure also causes high incidence of postoperative nausea &vomiting. Clonidine has been shown to reduce intraoperative haemodynamic instability. This randomized prospective single blind clinical study was designed to evaluate the efficacy of oral clonidine premedication for attenuation of haemodynamic responses associated with pneumoperitoneum & also reduce the incidence of postoperative nausea & vomiting and carried out in the department of Anaesthesia Analgesia and Intensive care Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from January 2010 to June 2010. Sixty adult patients of ASA physical status I & II scheduled for elective laparoscopic cholecystecomy were recruited for a prospective randomized single blind study. They were selected randomized into two equal groups, thirty in each group. Group A received no premedication & Group B (Clonidine) received clonidine (100μgm) orally 60 minutes before induction of anaesthesia. Pulse rate, mean arterial pressure were recorded prior to induction, 2 minutes after endotracheal intubation, before pneumoperitoneum, 10 minutes & 20 minutes after pneumoperitoneum, 10 minutes after release of carbon dioxide & 10 minutes after extubation. Patients in Group B (Clonidine) maintained greater haemodynamic stability intraoperatively compare to Group A after intubation, during pneumoperitoneum and also extubation. Pulse rate & mean arterial pressure significantly varies in Group A compare with Group B (Clonidine) at different times of intraoperative period (p<0.05). Postoperative nausea & vomiting was significantly less in Group B (Clonidine). Premedication with oral clonidine attenuates the haemodynamic responses produced by pneumoperitoneum during laparoscopic cholecystectomy and also significantly nausea and vomiting, results better patient satisfaction and cost effective.

Cerebral metabolic changes in men after chiropractic spinal manipulation for neck pain

BACKGROUND

Chiropractic spinal manipulation (CSM) is an alternative treatment for back pain. The autonomic nervous system is often involved in spinal dysfunction. Although studies on the effects of CSM have been performed, no chiropractic study has examined regional cerebral metabolism using positron emission tomography (PET).

OBJECTIVE

The aim of the present study was to investigate the effects of CSM on brain responses in terms of cerebral glucose metabolic changes measured by [18F]fluorodeoxyglucose positron emission tomography (FDG-PET).

METHODS

Twelve male volunteers were recruited. Brain PET scanning was performed twice on each participant, at resting and after CSM. Questionnaires were used for subjective evaluations. A visual analogue scale (VAS) was rated by participants before and after chiropractic treatment, and muscle tone and salivary amylase were measured.

RESULTS

Increased glucose metabolism was observed in the inferior prefrontal cortex, anterior cingulated cortex, and middle temporal gyrus, and decreased glucose metabolism was found in the cerebellar vermis and visual association cortex, in the treatment condition (P < .001). Comparisons of questionnaires indicated a lower stress level and better quality of life in the treatment condition. A significantly lower VAS was noted after CSM. Cervical muscle tone and salivary amylase were decreased after CSM. Conclusion The results of this study suggest that CSM affects regional cerebral glucose metabolism related to sympathetic relaxation and pain reduction.

Application of positron emission tomography in physical medicine

Positron emission tomography (PET) is widely used in the fields of clinical and basic medicine. The PET device utilizes coincidence logic to detect annihilation photons emitted from positrons and estimates physiological functions of human organs in vivo. Radiopharmaceutical 18F- fluorodeoxyglucose (FDG), an analogue of glucose, is trapped metabolically in cells after being administered into the body, and can be substantially used for evaluating physiological and biochemical functions in vivo. Here, we attempted to describe the basics of PET as well as to apply the technique together with 18F-FDG as a tracer for evaluating organ glucose metabolism induced by exercise. Three-dimensional (3D) FDG-PET was applied to normal volunteers who performed exercise to evaluate whole-body glucose metabolism. Regions of interest analysis were drawn on visually defined regions (i.e., lower limbs, thigh, liver, intestine, brain, heart, etc.) to determine radioactivity distribution. FDG-PET clearly showed the recruitment of energy resources from abdominal organs to lower limb skeletal muscles to balance energy expenditures. The results suggested that 3D FDG-PET can be applied as an imaging tool to physical medicine.

Effects of aerobic exercise training on brain structure and psychological well-being in young adults

AIM

There is convergent evidence that exercise increases psychological well-being; however, the mechanism of this psychological effect of exercise is not yet completely understood. The purpose of this study was to examine the effects of aerobic exercise training on brain structure and psychological well-being in young adults.

METHODS

University students who had not regularly exercised were divided into training group (N.=15) and control group (N.=15). The training group performed a total 30 periods of aerobic exercise training, while the control group never performed. Whole-brain magnetic resonance imaging scans and mental health questionnaire examinations were performed before and after the exercise training period for all of the participants. A voxel-based morphometry (VBM) analysis was used to compare the changes in gray-matter volumes in the two groups. VBM is an objective whole-brain technique for characterization of regional cerebral volume and tissue concentration differences in structural magnetic resonance images.

RESULTS

The results of VBM analysis revealed no change in gray-matter volume in the training group, although the gray-matter volume of the left insula was significantly decreased in the control group after the exercise training period. The training group exhibited significant improvement in some scores on the mental health questionnaire after the exercise training period, compared with the control group.

CONCLUSIONS

These findings suggest that aerobic exercise training may inhibit gray-matter volume loss in the insula, and that a relationship may exist between preservation of insula gray-matter and improvement of psychological well-being by aerobic exercise training.

Regional cerebral glucose metabolism of patients with malignant diseases in different clinical phases

BACKGROUND

Psychological and psychiatric aspects of cancer patients have not been studied well in terms of functional neuroimaging. The aim of this pilot study was to investigate the relationship between regional cerebral metabolism and different clinical phases.

MATERIAL AND METHODS

Relative cerebral glucose metabolism of 77 Japanese patients with various types of malignant diseases was studied by positron emission tomography with 18F-fluorodeoxyglucose. They were subgrouped into the 1) pre-treatment, 2) post-treatment, 3) recurrence and 4) terminal patient groups. These subgroups were compared to the control group of 17 in-patients with benign diseases, using voxel-based Statistic Parametrical Mapping software (SPM).

RESULTS

Relative reduction in the regional cerebral metabolism was detected in the prefrontal and basolateral (inferolateral) prefrontal cortex, orbitofrontal cortex, anterior and posterior cingulate gyrus, insula, basal ganglia, hippocampus and thalamus in the pre-treatment group. The anterior and posterior cingulate gyri were hypometabolic only in the pre-treatment group. Hypometabolic areas were detected only in the basolateral prefrontal cortex, orbitofrontal cortex, ventral part of cingulate gyrus and insula in the post-treatment group. In the terminal group, the hypometabolic pattern seemed very close to that in the pre-treatment group. The results seemed to suggest that hypometabolic findings before treatments could reverse after treatments but became prominent again in terminal stages. These results do not contradict with previous epidemiological findings that high incidence of adjustment disorders was seen in earlier stages and more brain organic syndromes were seen in terminal stages.

CONCLUSION

It could be speculated that decreased metabolism in early stages reflects state-dependent changes and that decreased metabolism in the terminal stages reflects organic brain damages. brain of cancer patients show a fluctuation in the regional metabolism. This finding might give a suggestion that functional imaging could be used as a supplementary method for psychological evaluation of patients with severe diseases.

Evaluation of resting brain conditions measured by two different methods (i.v. and oral administration) with18F-FDG-PET

Our aim was to evaluate regional differences between brain activity in two resting control conditions measured by 3D PET after administration of FDG through either the intravenous (i.v.) or the oral route. Ten healthy male volunteers engaged in the study as the i.v. group (mean age, 26 +/- 9.3 years, +/- S.D.) who received FDG intravenously and another 10 volunteers as the oral group (mean age, 27.9 +/- 11.3 years, +/- S.D.) who received FDG per os. A set of 3D-PET scans (emission and transmission scans) were performed in both groups. To explore possible functional differences between the brains of the two groups, the SPM-96 software was used for statistical analysis. The results revealed that glucose metabolism was significantly higher in the superior frontal gyrus, superior parietal lobule, lingual gyrus and left cerebellar hemisphere in the i.v. group than in the oral group. Metabolically active areas were found in the superior, middle and inferior temporal gyrus, parahippocampal gyrus, amygdaloid nucleus, pons and cerebellum in the oral group when compared with the i.v. group. These differences were presumably induced by differences between FDG kinetics and/or time-weighted behavioral effects in the two studies. This study suggests the need for extreme caution when selecting a pooled control population for designated activation studies.