Search research articles

Related Concept Videos

Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

DNA-Inspired Lightweight Cryptographic Algorithm for Secure and Efficient Image Encryption.

Sensors (Basel, Switzerland)·2025

Same author

Prediction of fetal brain gestational age using multihead attention with Xception.

Computers in biology and medicine·2024

Same author

Accurate Prediction of Anxiety Levels in Asian Countries Using a Fuzzy Expert System.

Healthcare (Basel, Switzerland)·2023

Same author

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm.

Healthcare (Basel, Switzerland)·2022

Same author

Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model.

Diagnostics (Basel, Switzerland)·2022

Same author

Slotted photonic crystal fiber-based plasmonic biosensor.

Applied optics·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 7, 2026

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

Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.

Meftahul Jannat¹, Shaikh Afnan Birahim², Mohammad Asif Hasan¹

¹Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.

Diagnostics (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study introduces a new deep learning model for precise lung segmentation in chest X-rays, aiding early disease detection. The Lightweight Residual U-Net achieves high accuracy, improving diagnostic capabilities for radiologists.

Keywords:

CXR images convolutional block attention module dice loss lightweight residual U-Net lung segmentation

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: May 7, 2026

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Chest radiograph (CXR) analysis is complex and time-consuming, often requiring identification of multiple anomalies.
Accurate lung segmentation is crucial for improving the efficiency and performance of deep learning (DL) models in CXR analysis.
Early detection of high-risk lung diseases necessitates precise identification of lung regions in CXR images.

Purpose of the Study:

To develop a DL-based approach for accurate lung mask segmentation in CXR images.
To assist radiologists in the early identification of signs of high-risk lung diseases.
To present a novel, efficient, and accurate lung segmentation model.

Main Methods:

A novel Lightweight Residual U-Net architecture was proposed, integrating Convolutional Block Attention Module (CBAM) and Atrous Spatial Pyramid Pooling (ASPP).
The model, comprising 3.24 million parameters, was trained using LeakyReLU activation and optimized with the Dice loss function.
The technique leverages DL and attention mechanisms for enhanced lung segmentation.

Main Results:

The proposed model achieved high Dice scores on benchmark datasets: 98.72% (JSRT), 97.49% (SZ), and 99.08% (MC).
Performance surpassed existing state-of-the-art models in lung segmentation accuracy.
LeakyReLU activation and Dice loss function demonstrated superior effectiveness.

Conclusions:

The developed DL model significantly enhances lung segmentation accuracy in CXR images.
The model's efficiency and precision aid in the early detection of serious lung diseases.
This approach represents an advancement in leveraging AI for medical diagnostics.