Chest CT Image based Lung Disease Classification - A Review.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Pulmonology

Background:

• Computed tomography (CT) scans are crucial for diagnosing lung conditions.
• Manual interpretation of CT scans can lead to diagnostic errors.
• Advanced systems are needed for accurate lung disease classification from CT images.

Purpose of the Study:

• To provide an extensive analysis of different approaches for lung disease classification using CT scans.
• To guide researchers in developing advanced diagnostic systems.
• To review current machine learning (ML) techniques and their performance in this field.

Main Methods:

• Overview of lung disease diagnosis and treatment procedures.
• Description of existing lung disease classification methods.
• Explanation of general procedures for ML-based lung disease classification.
• Review of recent advancements in ML for lung disease classification.

Main Results:

• Deep learning techniques have shown significant promise in revolutionizing early lung disorder identification.
• Analysis of various approaches and their performances is presented.
• Existing challenges in ML techniques for lung disease classification are discussed.

Conclusions:

• Deep learning significantly enhances early lung disorder detection.
• This work aims to improve medical professionals' awareness and classification abilities for lung disorders.
• Further development of advanced ML systems is crucial for accurate and timely diagnosis.