Pneumonia and COVID-19 Detection using Convolutional Neural Networks

Militante, Sammy V.; Dionisio, Nanette V.; Sibbaluca, Brandon G.

doi:10.1109/icvee50212.2020.9243290

Cited by 30 publications

(21 citation statements)

References 11 publications

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“…Implementation of facemask detection with alarm systems for physical distancing utilizing deep learning technique using CNN is discussed in [ 18 , 12 ]. The researchers introduced a high accuracy strategy for detecting facial masks based on fully convolutional networks, gradient descent, and binomial cross-entropy by using semantic segmentation.…”

Section: Related Workmentioning

confidence: 99%

IoT-Enabled smart doors for monitoring body temperature and face mask detection

Varshini

Yogesh

Pasha

et al. 2021

Global Transitions Proceedings

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COVID 19 pandemic is causing a global health epidemic. The most powerful safety tool is wearing a face mask in public places and everywhere else. The COVID 19 outbreak forced governments around the world to implement lockdowns to deter virus transmission. According to survey reports, wearing a face mask at public places reduces the risk of transmission significantly. In this paper, an IoT-enabled smart door that uses a machine learning model for monitoring body temperature and face mask detection. The proposed model can be used for any shopping mall, hotel, apartment entrance, etc. As an outcome a cost-effective and reliable method of using AI and sensors to build a healthy environment. Evaluation of the proposed framework is done by the Face Mask Detection algorithm using the TensorFlow software library. Besides, the body temperature of the individual is monitored using a non-contact temperature sensor. This proposed system can detect the users from COVID 19 by enabling the Internet of Things (IoT) technology.

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Section: Related Workmentioning

confidence: 99%

IoT-Enabled smart doors for monitoring body temperature and face mask detection

Varshini

Yogesh

Pasha

et al. 2021

Global Transitions Proceedings

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“…A lung mask was used to eliminate the image context of non-lung areas in chest CT to focus on the lungs . Sammy V. Militante in [4] proposed a VGG-16 model in their research to predict COVID-19 and pneumonia on X-ray scan images using the Convolutional Neural Network method. With this model, they have obtained 95% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

COVID-19 Prediction using X-Ray Images

Aparna

Gowri

Bharathi

et al. 2021

2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)

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Coronavirus disease (COVID-19) is a pandemic caused by the coronavirus S ARS -CoV-2 that was not previously seen in humans. COVID-19 is spreading rapidly throughout the world. COVID-19 can be detected by a lung infection of the patients. The standard method for detecting COVID-19 is the Reverse transcription-polymerase chain reaction (RT-PCR) test. But the availability of RT-PCR tests is in short supply. As a result of this, the early detection of the disease is difficult. The easily obtainable modes like X-rays are often used for detecting infections in the lungs. It is confirmed that X-ray scans can be widely used for efficient COVID-19 diagnosis. But a physical diagnosis of X-rays of an outsized number of patients is a longterm process. A deep learning-based diagnosis process can help radiologists in detecting COVID-19 from X-ray scans. Pretrained CNNs are commonly used in detecting diseases from datasets. This paper proposes a CNN model with a parallelization strategy that extracts the features in the X-ray images by applying filters parallelly through the images. Our proposed method aims to attain higher accuracy and a less loss rate with precision. To do so, the accuracy and loss rates of three types of CNN -VGG-16, MobileNet, and CNN are compared with the parallelization technique. S ince, VGG-16 and MobileNet are pretrained models; those two models are directly imported from Keras. Moreover, this paper utilizes two datasets consisting of COVID X-ray images and Non-COVID X-ray images for the prediction of COVID-19 using Convolution Neural Network [CNN].

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“…CNN is applied precisely in image-processing [10], processing of humanlanguage, computer-vision [11], self-driving automobiles. The CNNs is also a particular form of neural network architecture that depicts a conventional feed-forward neural network; it simulates a human visual processing cortex of the brain, where the filtration system is a network of cells that resembles a certain portion of a picture [7]. A convolutional-layer in a neural network consists of a filter that converts over the input according to the particular size of the filter and the stride width [5].…”

Section: A the Cnns Or Convolutional Neural Networkmentioning

confidence: 99%

“…The Government and most organizations are making it compulsory to follow social distancing and the wearing of a facemask. Facemask detection system through artificial intelligence is a ground-breaking technological answer that is beneficial to everyone is recognizing people wearing facemasks [7]. A new normal has emerged in this pandemic by covering faces with a mask that is very effective in the prevention of the disease outbreak.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems

Militante

Dionisio

2020

2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE)

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COVID-19 or Severe Acute Respiratory Syndrome Corona virus-2 is an extremely transmissible virus that is discharged through breathing droplets released from an infected individual who is talking, sneezing, or coughing. Close interaction with a person infected or through touching a contaminated surface and object can spread the virus rapidly. As of now, there is no vaccine to combat the COVID-19, and the best way to protect the person from a virus is to avoid being exposed to it. Wearing a facemask that covers the nose and mouth in a public setting and repeatedly cleansing of hands or the use of at least 70% alcohol-based disinfectants is a practice to avoid virus exposure. Deep Learning technology has demonstrated its achievement in recognition and classification by processing images. The research study uses deep learning techniques that identify if the person is wearing a facemask or not and check if the persons in the area observe physical distancing. The collected image data contains 20,000 images, uniformly crop images in 224x224 pixels, and attained an accuracy rate of 97% during the training of the model. The developed system is implemented using Python and OpenCV through TensorFlow that recognizes persons wearing a facemask or not wearing and measures the physical distance between each person. It signals an alarm and captures facial images upon detecting persons not wearing a mask and does not observe physical distancing. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.

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Pneumonia and COVID-19 Detection using Convolutional Neural Networks

Cited by 30 publications

References 11 publications

IoT-Enabled smart doors for monitoring body temperature and face mask detection

IoT-Enabled smart doors for monitoring body temperature and face mask detection

COVID-19 Prediction using X-Ray Images

Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems

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