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Updated: Jul 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

COVID-19 infection segmentation using hybrid deep learning and image processing techniques.

Samar Antar¹, Hussein Karam Hussein Abd El-Sattar², Mohammad H Abdel-Rahman¹

¹Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt.

Scientific Reports

|December 20, 2023

Summary

This study introduces a novel deep learning approach for accurate COVID-19 detection using processed CT scan images. The method enhances U-Net segmentation by utilizing RGB channels, achieving high accuracy in identifying the virus.

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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

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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

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Traditional medical imaging analysis for COVID-19 faces challenges in accuracy and segmentation.
Deep learning offers potential for improving diagnostic capabilities in medical imaging.

Purpose of the Study:

To develop and evaluate a novel deep learning approach for enhanced COVID-19 detection in lung images.
To improve the accuracy and efficiency of identifying COVID-19 infections using computed tomography (CT) scans.
To leverage image processing techniques and U-Net architecture for precise infection segmentation.

Main Methods:

Preprocessing CT images using thresholding and resizing to 128x128 pixels.
Applying a density heat map for coloring, followed by RGB channel separation.
Utilizing three U-Net models for independent channel segmentation and combining results via convolution.

Main Results:

The proposed method achieved high performance metrics: 99.71% accuracy, 0.83 sensitivity, 0.87 precision, and 0.85 dice coefficient.
Coloring CT images and processing RGB channels improved U-Net segmentation effectiveness.
The approach demonstrated accurate detection on smaller 128x128 images compared to methods using larger 512x512 images.

Conclusions:

The novel deep learning method effectively enhances COVID-19 detection through image processing and U-Net segmentation.
Processing RGB channels of colored CT images significantly boosts detection accuracy and segmentation power.
This technique offers a rapid and highly accurate solution for COVID-19 diagnosis using medical imaging.