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Pneumothorax-II

Pneumothorax-II

Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:

Pneumothorax-I

Pneumothorax-I

A pneumothorax is a condition where air builds up in the space between the lung and the chest wall, causing the lung to collapse. This condition arises when air enters the space between the parietal and visceral pleura, disrupting the negative pressure essential for lung inflation. This can lead to a partial or complete collapse of the lung.
Pneumothorax can be even further classified as spontaneous, traumatic, and tension pneumothorax.

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

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Updated: Sep 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

Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images.

Priyanka Malhotra¹, Sheifali Gupta¹, Deepika Koundal²

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Patiala 140401, Punjab, India.

Sensors (Basel, Switzerland)

|March 26, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning model using Mask RCNN with ResNet101 for detecting pneumothorax on chest X-rays. The proposed model demonstrated superior performance over a ResNet50-based model in identifying this critical respiratory condition.

Keywords:

deep learning image segmentation mask RCNN medical imaging pneumothorax

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Area of Science:

Medical Imaging
Artificial Intelligence
Thoracic Diseases

Background:

Pneumothorax is a serious thoracic disease that can lead to respiratory failure, cardiac arrest, and death.
Chest X-ray (CXR) is the primary diagnostic tool for pneumothorax.
Computerized systems offer significant benefits for disease diagnosis in radiographic images.

Purpose of the Study:

To propose a deep learning neural network model for detecting pneumothorax regions in chest X-ray images.
To evaluate the performance of a Mask RCNN model with ResNet101 backbone against a ResNet50-based model.

Main Methods:

A Mask Regional Convolutional Neural Network (Mask RCNN) framework was employed.
Transfer learning utilizing ResNet101 as a backbone feature pyramid network (FPN) was implemented.
The model was trained on the SIIM-ACR pneumothorax dataset.

Main Results:

The proposed ResNet101-based Mask RCNN model exhibited lower class loss, bounding box loss, and mask loss compared to the ResNet50-based model.
Both models were simulated with learning rates of 0.0004 and 0.0006 over 10 and 12 epochs, respectively.

Conclusions:

The Mask RCNN model with ResNet101 as an FPN demonstrates improved performance in detecting pneumothorax compared to a ResNet50-based model.
Deep learning approaches, specifically Mask RCNN, show promise for enhancing the accuracy and efficiency of pneumothorax diagnosis from chest X-rays.