Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied

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COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

Applied Sciences ◽

10.3390/app10165683 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5683 ◽

Cited By ~ 1

Author(s):

Lourdes Duran-Lopez ◽

Juan Pedro Dominguez-Morales ◽

Jesús Corral-Jaime ◽

Saturnino Vicente-Diaz ◽

Alejandro Linares-Barranco

Keyword(s):

Deep Learning ◽

Imaging Techniques ◽

Learning System ◽

Screening Process ◽

X Ray ◽

Average Accuracy ◽

Rapid Spread ◽

Chest X Ray ◽

And Control ◽

Aided Diagnosis

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

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A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i6.pp5530-5540 ◽

2021 ◽

Vol 11 (6) ◽

pp. 5530

Author(s):

Ihssan S. Masad ◽

Amin Alqudah ◽

Ali Mohammad Alqudah ◽

Sami Almashaqbeh

Keyword(s):

Deep Learning ◽

Hybrid System ◽

Nearest Neighbor ◽

Intelligent System ◽

Detection System ◽

Learning System ◽

Support Vector ◽

K Nearest Neighbor ◽

X Ray ◽

Chest X Ray

<span>Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.</span>

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Real-Time Face Mask Identification Using Facemasknet Deep Learning Network

SSRN Electronic Journal ◽

10.2139/ssrn.3663305 ◽

2020 ◽

Author(s):

Madhura Inamdar ◽

Ninad Mehendale

Keyword(s):

Deep Learning ◽

Real Time ◽

Face Mask ◽

Learning Network ◽

Deep Learning Network

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Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317825 ◽

2020 ◽

pp. bjophthalmol-2020-317825

Author(s):

Yonghao Li ◽

Weibo Feng ◽

Xiujuan Zhao ◽

Bingqian Liu ◽

Yan Zhang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

High Myopia ◽

Large Scale ◽

Learning System ◽

Youden Index ◽

Optical Coherence ◽

Test Dataset ◽

Independent Test ◽

Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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Deep Learning based Face Mask Recognition System -A Review

2021 6th International Conference on Inventive Computation Technologies (ICICT) ◽

10.1109/icict50816.2021.9358515 ◽

2021 ◽

Author(s):

Priya. K Hari ◽

S Malathi

Keyword(s):

Deep Learning ◽

Face Mask ◽

Recognition System ◽

System A

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Face Mask Detection Using Deep Learning

2021 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci50826.2021.9402670 ◽

2021 ◽

Author(s):

Ravi Kishore Kodali ◽

Rekha Dhanekula

Keyword(s):

Deep Learning ◽

Face Mask

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A deep-learning system to classify lung X-ray images into normal/pneumonia class

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2020.09.556 ◽

2020 ◽

Vol 101 ◽

pp. 209

Author(s):

R. Baskaran ◽

B. Ajay Rajasekaran ◽

V. Rajinikanth

Keyword(s):

Deep Learning ◽

Learning System ◽

X Ray

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Real Time Face Mask Identification Using Deep Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1916/1/012077 ◽

2021 ◽

Vol 1916 (1) ◽

pp. 012077

Author(s):

M Sujaritha ◽

S Kabilan ◽

M Manikandan ◽

S Nanda Kisore

Keyword(s):

Deep Learning ◽

Real Time ◽

Face Mask

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Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽

10.1055/a-1311-8570 ◽

2020 ◽

Author(s):

Alanna Ebigbo ◽

Robert Mendel ◽

Tobias Rückert ◽

Laurin Schuster ◽

Andreas Probst ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Pilot Study ◽

White Light ◽

Submucosal Invasion ◽

Learning System ◽

Endoscopic Images ◽

Barrett’S Cancer ◽

Significant Difference ◽

Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

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