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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: Jul 29, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images.

Chalapathiraju Kanumuri¹, Renu Madhavi Chodavarapu²

¹Electronics and Communication Engineering, S.R.K.R Engineering College, Bhimavaram, Andhra Pradesh India.

New Generation Computing

|May 25, 2023

Summary

This study introduces a deep learning model for detecting COVID-19 from X-ray images. The model accurately classifies COVID-19 and over 20 pneumonia types, aiding early diagnosis and disease control.

Keywords:

CNN Covid-19 Diagnosis Pneumonia infection Radiograph World Health Organization

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Disease Diagnostics

Background:

The COVID-19 pandemic, declared by the World Health Organization (WHO), necessitates rapid and accurate diagnostic tools.
Distinguishing COVID-19 from other forms of pneumonia using medical imaging is crucial for effective treatment and containment.
Pneumonia encompasses over 20 classifications, including bacterial, fungal, and viral types, with COVID-19 falling under viral pneumonia.

Purpose of the Study:

To develop and evaluate a deep learning model for the early detection and classification of COVID-19 from radiographic images (X-rays).
To differentiate COVID-19 from various other types of pneumonia and identify healthy cases.
To provide a user-friendly graphical user interface (GUI) for flexible model execution.

Main Methods:

Utilized a deep learning (DL) approach, specifically a convolutional neural network (CNN) trained on ImageNet, adapted as a feature extractor for radiographic images.
The CNN model was trained on 21 types of pneumonia radiographs and integrated with artificial intelligence (AI) strategies.
A graphical user interface (GUI) was developed for enhanced usability and flexibility in model application.

Main Results:

The proposed model achieved an accuracy of 92% in classifying over 20 types of pneumonia infections.
The model demonstrated effective distinction between COVID-19 positive cases and other pneumonia types from radiographs.
Early detection of COVID-19 was facilitated, enabling timely patient isolation and disease spread mitigation.

Conclusions:

The developed deep learning model shows high accuracy in identifying COVID-19 from X-ray images, outperforming other pneumonia classifications.
This AI-driven approach offers a valuable tool for early COVID-19 detection, supporting public health efforts.
The GUI integration enhances the practical applicability of the model in clinical settings for improved diagnostic workflows.