COVID-19: Automatic Detection of the Novel Coronavirus Disease from CT Images Using an Optimized Convolutional Neural Network

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19) Automatic detection of COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

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Orthonormal Bayesian Convolutional Neural Network for Detection of the Novel Coronavirus-19

Lecture Notes in Electrical Engineering - Innovations in Electrical and Electronic Engineering ◽

10.1007/978-981-16-0749-3_63 ◽

2021 ◽

pp. 819-836

Author(s):

Romin Chetankumar Jariwala ◽

Madhusudana Rao Nalluri

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

The Novel ◽

Novel Coronavirus

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DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-893-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 893-900 ◽

Cited By ~ 21

Author(s):

R. Pierdicca ◽

E. S. Malinverni ◽

F. Piccinini ◽

M. Paolanti ◽

A. Felicetti ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Photovoltaic Cells ◽

Automatic Detection ◽

Thermal Infrared ◽

The Novel ◽

Infrared Sensor ◽

Novel Approach ◽

Safety And Stability ◽

Degradation Problem

The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

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Automatic detection and classification of rib fractures based on patients’ CT images and clinical information via convolutional neural network

European Radiology ◽

10.1007/s00330-020-07418-z ◽

2020 ◽

Author(s):

Qing-Qing Zhou ◽

Wen Tang ◽

Jiashuo Wang ◽

Zhang-Chun Hu ◽

Zi-Yi Xia ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Clinical Information ◽

Automatic Detection ◽

Ct Images ◽

Rib Fractures

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Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network

10.1101/2020.11.08.20228080 ◽

2020 ◽

Author(s):

Sohaib Asif ◽

Kamran Amjad

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

X Rays ◽

The Novel ◽

Rt Pcr ◽

Radiographic Images ◽

Detection Systems ◽

Global Pandemic ◽

Novel Coronavirus

AbstractThe global pandemic of the novel coronavirus that started in Wuhan, China has affected more than 50 million people worldwide and caused more than 1263,787 tragic deaths. To date, the COVID-19 virus is still spreading and affecting thousands of people. The main problem with testing for COVID-19 is that there are very few test kits available for a large number of affected or suspicious individuals. This leads to the need for automatic detection systems that use artificial intelligence. Deep learning is one of the most powerful AI tools available, so we recommend creating a convolutional neural network to detect COVID-19 positive patients from chest radiographs. According to previous studies, lung X-rays of COVID-19-positive patients show obvious characteristics, so this is a reliable method for testing patients, because X-ray examination of suspicious patients is easier than rt-PCR. Our model has been trained with 820 chest radiographic images (excluding data augmentation) collected from 3 databases, with a classification accuracy of 99.45% (training accuracy of 99.70%), sensitivity of 99.30% and specificity of 99.40 %, proved that our model has become a reliable COVID-19 detector.

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Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

2019 ◽

Vol 13 ◽

Author(s):

Niha Kamal Basha ◽

Aisha Banu Wahab

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Monitoring System ◽

Single Channel ◽

Confusion Matrix ◽

Sudden Change ◽

Automatic Detection ◽

Support Vector ◽

Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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