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Related Experiment Video

Updated: Oct 15, 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

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

M Rubaiyat Hossain Mondal¹, Subrato Bharati¹, Prajoy Podder¹

¹Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

|October 28, 2021

Summary

This summary is machine-generated.

This study introduces an optimized deep learning model, CO-IRv2, for diagnosing coronavirus disease (COVID-19) using CT scans. The CO-IRv2 model with the Nadam optimizer achieved high accuracy, outperforming existing deep learning methods for COVID-19 detection.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate and timely diagnosis of COVID-19 is crucial for effective patient management and disease control.
Deep learning (DL) models have shown promise in medical image analysis, but novel architectures are needed for improved COVID-19 detection.

Purpose of the Study:

To develop and evaluate an optimized deep learning model, CO-IRv2, for the diagnosis of COVID-19 using computed tomography (CT) and X-ray images.
To compare the performance of different optimizers (Adam, Nadam, RMSProp) within the CO-IRv2 framework.

Main Methods:

A novel deep learning architecture, CO-IRv2, was developed by combining InceptionNet and ResNet concepts with hyperparameter tuning and additional layers.
The CO-IRv2 model was trained and evaluated on a combined dataset of 2481 CT images and a separate dataset of 1662 X-ray images.
Performance was assessed using metrics like accuracy, precision, recall, F1-score, and AUC, with Adam, Nadam, and RMSProp optimizers evaluated.

Main Results:

For CT images, CO-IRv2 achieved accuracies of 94.97% (Adam), 96.18% (Nadam), and 96.18% (RMSProp).
The CO-IRv2 model with the Nadam optimizer demonstrated superior performance compared to existing DL algorithms for COVID-19 diagnosis on CT images.
On X-ray images, CO-IRv2 achieved a high classification accuracy of 99.40%.

Conclusions:

The proposed CO-IRv2 deep learning model, particularly with the Nadam optimizer, is highly effective for diagnosing COVID-19 from CT and X-ray images.
Optimized deep learning approaches offer a promising avenue for enhancing the accuracy and efficiency of infectious disease diagnosis.