Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 2 | Issue - 3 | september 2020
Published
18 June, 2020
There is a rapid spread of the novel corona virus (Covid-19) among millions of people and causing the death of hundreds of thousands of people according to the analytical data provided by the European Centre for Disease Prevention and Control. However, the number of test kits available for Covid-19 is still limited despite the continuously increasing cases every day. Implementation of an automatic detection system is essential for diagnosis and prevention of the spread of Covid-19. Chest X-ray radiographs are used for the detection of Corona Virus using three significant models of convolution neural network namely Inception- ResNetV2, InceptionV3 and ResNet50. Among the existing systems, the highest performance and classification accuracy is provided by the ResNet50 model. A novel framework based on CNN model is proposed that offers improved specificity, sensitivity and accuracy when compared to the existing models. Fivefold cross validation is used for analysis of the existing models and comparison of the proposed model by means of confusion matrices and ROC analysis.
KeywordsDeep Transfer Learning Convolutional Neural Network Chest X-ray Radiographs Pneumonia Corona Virus
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