Javascript must be enabled to continue!
DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES
View through CrossRef
The Coronavirus disease outbreak result
in many people to have severe respira- tory
problems and it was recognized as a global health
threat. Since the virus is targeting the lungs in the
human body initially, chest x-ray imaging features
were considered to be useful for the detection of the
infection in the early stage. In this study, the chest
x-ray data of 130 infected patients from an open
data source that referenced Cohen J. Morrison P.
Dao L., 2020 was used to build a CNN(
Convolutional Neural-Network) model for the
early detection of the disease. The model was
trained with both infected and not-infected
peoples’ chest x-ray images with 100 epochs which
led to 0.98 accuracy finally. In order to use this
model as a professional diagnosis element, it is
highly recommended it be improved with more
images and the model can be restructured to get a
better accuracy.
Title: DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES
Description:
The Coronavirus disease outbreak result
in many people to have severe respira- tory
problems and it was recognized as a global health
threat.
Since the virus is targeting the lungs in the
human body initially, chest x-ray imaging features
were considered to be useful for the detection of the
infection in the early stage.
In this study, the chest
x-ray data of 130 infected patients from an open
data source that referenced Cohen J.
Morrison P.
Dao L.
, 2020 was used to build a CNN(
Convolutional Neural-Network) model for the
early detection of the disease.
The model was
trained with both infected and not-infected
peoples’ chest x-ray images with 100 epochs which
led to 0.
98 accuracy finally.
In order to use this
model as a professional diagnosis element, it is
highly recommended it be improved with more
images and the model can be restructured to get a
better accuracy.
Related Results
Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution
Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution
Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken fr...
Phenotype to genotype mapping using supervised and unsupervised learning
Phenotype to genotype mapping using supervised and unsupervised learning
AbstractThe relationship between the genotype, the genetic instructions encoded into a genome, and phenotype, the macroscopic realization of such instructions, remains mostly uncha...
A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment
A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment
Agricultural droughts can cause many serious hazards. Drought monitoring indices, namely Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (...
Analysis of COVID-19 Awareness Among Georgia Private High School Students
Analysis of COVID-19 Awareness Among Georgia Private High School Students
Throughout the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) has updated the public on pertinent information regarding COVID-19...
Real Time Person Detection and Classification using YOLO
Real Time Person Detection and Classification using YOLO
A Convolutional Neural Network (CNN) is a class of deep neural network most commonly used in analyzing visual images. Various systems and applications have been built to detect and...
Application of YOLOX deep learning model for automated object detection on thermograms
Application of YOLOX deep learning model for automated object detection on thermograms
A method of automating the data analysis of thermal imaging systems in the field of safety control is proposed. It has been established that today video surveillance technologies h...
Fusion of Machine learning for Detection of Rumor and False Information in Social Network
Fusion of Machine learning for Detection of Rumor and False Information in Social Network
In recent years, spreading social media platforms and mobile devices led to more social data, advertisements, political opinions, and celebrity news proliferating fake news. Fake n...
Learning with ANIMA
Learning with ANIMA
The paper develops a semi-formal model of learning which modifies the traditional paradigm of artificial neural networks, implementing deep learning by means of a key insight borro...