A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

Sunil Kumar; Harish Kumar; Gyanendra Kumar; Shailendra Pratap Singh; Anchit Bijalwan; Manoj Diwakar

doi:10.1186/s12880-024-01192-w

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BMC Med Imaging. 2024 Feb 1;24(1):30. doi: 10.1186/s12880-024-01192-w.

Authors

Sunil Kumar^{1

2}, Harish Kumar¹, Gyanendra Kumar³, Shailendra Pratap Singh⁴, Anchit Bijalwan⁵, Manoj Diwakar⁶

Affiliations

¹ Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India.
² Department of Information Technology, School of Engineering and Technology (UIET), CSJM University, Kanpur, India.
³ Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.
⁴ School of Computer Engineering and Technology, Bennet University, Greater Noida, India.
⁵ Faculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia. anchit.bijalwan@amu.edu.et.
⁶ Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, India.

Abstract

Background: Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.

Objective: This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.

Methods: The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.

Results: This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

Keywords: CNN; CT scan; Deep learning; Ensemble learning; Transfer learning; X-ray.

Publication types

Review

MeSH terms

COVID-19* / diagnostic imaging
Humans
Lung Diseases* / diagnostic imaging
Lung Neoplasms* / diagnostic imaging
Machine Learning
Neural Networks, Computer