Discovering genomic patterns in SARS‐CoV‐2 variants

D’Angelo, Gianni; Palmieri, Francesco

doi:10.1002/int.22268

Cited by 19 publications

(12 citation statements)

References 20 publications

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“…The proposed method has high significance in the practical application of AD. The combination of AD susceptibility gene detection and pattern recognition is our future research direction [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

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Section: Discussionmentioning

confidence: 99%

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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“…A new pneumonia epidemic called acute respiratory syndrome coronavirus 2 (COVID- 19) started in Wuhan in the Hubei province of China in December 2019 [1][2][3]. COVID-19 rapidly expanded from a single city to the whole country within 30 days.…”

Section: Introductionmentioning

confidence: 99%

VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

Karacı

2022

Neural Comput & Applic

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X-ray images are an easily accessible, fast, and inexpensive method of diagnosing COVID-19, widely used in health centers around the world. In places where there is a shortage of specialist doctors and radiologists, there is need for a system that can direct patients to advanced health centers by pre-diagnosing COVID-19 from X-ray images. Also, smart computer-aided systems that automatically detect COVID-19 positive cases will support daily clinical applications. The study aimed to classify COVID-19 via X-ray images in high precision ratios with pre-trained VGG19 deep CNN architecture and the YOLOv3 detection algorithm. For this purpose, VGG19, VGGCOV19-NET models, and the original Cascade models were created by feeding these models with the YOLOv3 algorithm. Cascade models are the original models fed with the lung zone X-ray images detected with the YOLOv3 algorithm. Model performances were evaluated using fivefold cross-validation according to recall, specificity, precision, f1-score, confusion matrix, and ROC analysis performance metrics. While the accuracy of the Cascade VGGCOV19-NET model was 99.84% for the binary class (COVID vs. no-findings) data set, it was 97.16% for the three-class (COVID vs. no-findings vs. pneumonia) data set. The Cascade VGGCOV19-NET model has a higher classification performance than VGG19, Cascade VGG19, VGGCOV19-NET and previous studies. Feeding the CNN models with the YOLOv3 detection algorithm decreases the training test time while increasing the classification performance. The results indicate that the proposed Cascade VGGCOV19-NET architecture was highly successful in detecting COVID-19. Therefore, this study contributes to the literature in terms of both YOLO-aided deep architecture and classification success.

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“…Numerous countermeasures have been undertaken in the last months to cope with the virus pandemic, pending the long-awaited vaccine. Although the biological and medical fields are the more active ones [ 47 ], many other disciplines are involved in providing useful support to the issue [ 13 , 21 ]. Under this aspect, the COVID-19 has changed how the world does science.…”

Section: Introductionmentioning

confidence: 99%

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

D’Angelo

Palmieri

2021

Neural Comput & Applic

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With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

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Discovering genomic patterns in SARS‐CoV‐2 variants

Cited by 19 publications

References 20 publications

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

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