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 - 5 | Issue - 4 | december 2023
Published
03 January, 2024
The novel coronavirus (commonly abbreviated as CoVID-19) has emerged as a threat to the entire global civilization and has emerged as one of the most infectious and, at times, deadly viruses. Prompt discovery of this infection can assist medical supervisors in taking preventive actions to control the spread. Usually, radiologists and medical specialists require an average time of ~31 minutes to test the CT images and confirm the infection. A large dataset of more than 1000 patients has been gathered and randomly chosen for this experiment. In this research, a ready-to-deploy computer-aided diagnosis (CADx) to detect COVID-19 infection is introduced. A variety of deep learning architectures have been experimented to discover the most reliable predictive model for the diagnosis. This research uses the Densely Connected Convolution Network (DenseNet-121 architecture) along with a boosting support vector binary classifier to tell the difference between someone who has the coronavirus and someone who is healthy. The combination put forward in this work achieved 93% ± 1.8% accuracy, 94.9% ± 2.6% recall, 98% ± 1.5% precision, and an F1 score of 94% ± 1.7%. The model takes less than 1 second to process one image. On the grounds of the above findings, it can be concluded that the proposed approach can be used to diagnose novel coronavirus infections.
KeywordsChest CT DenseNet-121 COVID-19 Support Vector Machine Classifier Deep Learning
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