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Updated: Jul 25, 2025

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

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Published on: February 21, 2025

Review on chest pathogies detection systems using deep learning techniques.

Arshia Rehman¹, Ahmad Khan¹, Gohar Fatima²

¹COMSATS University Islamabad, Abbottabad-Campus, Abbottabad, Pakistan.

Artificial Intelligence Review

|June 26, 2023

Summary

This summary is machine-generated.

Deep learning significantly enhances automatic chest disease detection using computer-aided methods. This review synthesizes recent advancements in deep learning for chest radiography analysis, offering insights into current trends and future directions.

Keywords:

Chest pathologies Chest radiography Classification Datasets Deep learning Feature extraction Image acquisition

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Computer-Aided Diagnosis

Background:

Chest radiography is a primary, cost-effective tool for diagnosing thoracic diseases.
Expert radiologists traditionally interpret radiographs, but computer-aided methods enhance accuracy and speed.
Deep learning has revolutionized automated detection and analysis of chest pathologies.

Purpose of the Study:

To conduct a comprehensive review and technical evaluation of computer-aided chest pathology detection systems.
To synthesize the state-of-the-art in single and multi-pathology detection using deep learning.
To present a taxonomy of methodologies in automated chest disease analysis.

Main Methods:

Systematic review of deep learning-based computer-aided detection systems published in the last five years.
Discussion of image acquisition, dataset preprocessing, and feature extraction techniques.
Analysis of deep learning model architectures and their mathematical underpinnings.

Main Results:

Detailed comparison of systems based on contributions, datasets, methodologies, and performance metrics.
Identification of key advancements in single and multi-pathology detection.
Synthesis of current trends and challenges in the field.

Conclusions:

Deep learning models show significant promise for automated chest pathology detection.
Standardization of datasets and evaluation metrics is crucial for future progress.
Future research should focus on addressing current challenges and exploring novel deep learning architectures.