scholarly journals Fusion of Moment Invariant Method and Deep Learning Algorithm for COVID-19 Classification

2021 ◽  
Vol 5 (4) ◽  
pp. 74
Author(s):  
Ervin Gubin Moung ◽  
Chong Joon Hou ◽  
Maisarah Mohd Sufian ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Jamal Ahmad Dargham ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Changes ◽  
Learning Algorithm ◽  
Classification Models ◽  
Computerised Tomography ◽  
Moment Invariant ◽  
Health Crisis ◽  
Deep Learning Algorithm ◽  
Hu Moments ◽  
Rapid Spread

The COVID-19 pandemic has resulted in a global health crisis. The rapid spread of the virus has led to the infection of a significant population and millions of deaths worldwide. Therefore, the world is in urgent need of a fast and accurate COVID-19 screening. Numerous researchers have performed exceptionally well to design pioneering deep learning (DL) models for the automatic screening of COVID-19 based on computerised tomography (CT) scans; however, there is still a concern regarding the performance stability affected by tiny perturbations and structural changes in CT images. This paper proposes a fusion of a moment invariant (MI) method and a DL algorithm for feature extraction to address the instabilities in the existing COVID-19 classification models. The proposed method incorporates the MI-based features into the DL models using the cascade fusion method. It was found that the fusion of MI features with DL features has the potential to improve the sensitivity and accuracy of the COVID-19 classification. Based on the evaluation using the SARS-CoV-2 dataset, the fusion of VGG16 and Hu moments shows the best result with 90% sensitivity and 93% accuracy.

scholarly journals COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification

2020 ◽  
Author(s):  
Peter T. Habib ◽  
Alsamman M. Alsamman ◽  
Maha Saber-Ayad ◽  
Sameh E. Hassanein ◽  
Aladdin Hamwieh
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Virus Family ◽  
Health Crisis ◽  
Virulence Proteins ◽  
Genome Data ◽  
Deep Learning Algorithm ◽  
Severity Of The Disease ◽  
Novel Coronavirus

AbstractCOVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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