Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.INDEX TERMS Artificial intelligence, COVID-19 pneumonia, machine learning, transfer learning, viral pneumonia, computer-aided diagnostic tool.The associate editor coordinating the review of this manuscript and approving it for publication was Xin Zhang .

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11 %, 94.55 %, 94.56 %, 94.53 %, and 95.59 % respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

We characterize a compact MR-compatible PET insert for simultaneous preclinical PET/MRI. Although specifically designed with the strict size constraint to fit inside the 114-mm inner diameter of the BGA-12S gradient coil used in the BioSpec 70/20 and 94/20 series of small-animal MRI systems, the insert can easily be installed in any appropriate MRI scanner or used as a stand-alone PET system. The insert consists of a ring of 16 detector-blocks each made from depth-of-interaction-capable dual-layer-offset arrays of cerium-doped lutetium-yttrium oxyorthosilicate crystals read out by silicon photomultiplier arrays. Scintillator crystal arrays are made from 22 × 10 and 21 × 9 crystals in the bottom and top layers, respectively, with respective layer thicknesses of 6 and 4 mm, arranged with a 1.27-mm pitch, resulting in a useable field of view 28 mm long and about 55 mm wide. Spatial resolution ranged from 1.17 to 1.86 mm full width at half maximum in the radial direction from a radial offset of 0-15 mm. With a 300- to 800-keV energy window, peak sensitivity was 2.2% and noise-equivalent count rate from a mouse-sized phantom at 3.7 MBq was 11.1 kcps and peaked at 20.8 kcps at 14.5 MBq. Phantom imaging showed that features as small as 0.7 mm could be resolved. F-FDG PET/MR images of mouse and rat brains showed no signs of intermodality interference and could excellently resolve substructures within the brain. Because of excellent spatial resolvability and lack of intermodality interference, this PET insert will serve as a useful tool for preclinical PET/MR.

Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.

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Gamification is the use of game elements in domains other than games. Gamification use is often suggested for difficult activities because it enhances users’ engagement and motivation level. Due to such benefits, the use of gamification is also proposed in education environments to improve students’ performance, engagement, and satisfaction. Computer science in higher education is a tough area of study and thus needs to utilize various already explored benefits of gamification. This research develops an empirical study to evaluate the effectiveness of gamification in teaching computer science in higher education. Along with the learning outcomes, the effect of group size on students’ satisfaction level is also measured. Furthermore, the impact of gamification over time is analyzed throughout a semester to observe its effectiveness as a long-term learning technique. The analysis, covering both learning outcome and students’ satisfaction, suggests that gamification is an effective tool to teach tough courses at higher education level; however, group size should be taken into account for optimal classroom size and better learning experience.

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