Integrated Detection for Covid by using x- ray images
Year: 2024
Type: article
Abstract: The project "Integrated Detection for Covid" develops a machine learning-based system to detect COVID-19 using chest X-rays. It uses convolutional neural networks (CNNs) to classify images as "COVID" or "Normal," offering a fast and accessible screening tool to support healthcare providers, especially in areas with limited RT-PCR testing.Key Features:Automated Detection: Quick and accurate classification of chest X-rays.High Performance: Achieves reliable accuracy in distinguishing COVID-19 cases.User-Friendly Interface: Enables easy use for healthcare professionals.Methodology:The system involves data collection, preprocessing, training a CNN model, evaluation using metrics like accuracy, and deploying a simple user interface.Objectives:Primary Goal: Develop a reliable machine learning model capable of high-accuracy classification of chest X-rays into "COVID" or "Normal."Efficiency: Provide a tool that facilitates rapid preliminary screening in environments with limited testing resources.Support for Healthcare: Assist professionals with automated insights, reducing reliance on manual interpretations.Results:Achieved high accuracy in distinguishing between "COVID" and "Normal" cases.Demonstrated effective performance in rapid screening and decision-making.
Source: Figshare
Author: kamble, chetan
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6958439860
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