ItemConference Paper

A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques

Title
A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques
Creator
Kumar, Manish; Shanbhog, Manjula; Alapatt, Bosco Paul; Upreti, Kamal; Poonia, Ramesh Chandra
Description
Pneumonia is an infection that results in inflammation of the lungs and, if not identified in time, can be life-threatening. The most frequent method of diagnosing pneumonia is chest X-rays; the pictures are scrutinized closely. Pneumonia is still a global health burden. The accurate and timely diagnosis is difficult, especially in low-resource settings. X-rays have served as a primary key for the identification of pneumonia for many years. However, with the recent advancement in artificial intelligence technologies, especially deep learning and machine learning, there's now a potential to automatically detect and classify pneumonia using chest x-ray images. This review examines the research from 2019 to 2024 to understand the current trends and future direction in various deep learning and machine learning models. These encompass the convolutional neural networks, transfer learning methods, combined network designs, explainable AI models, and the use of radiomics with conventional machine learning techniques. However, the three significant challenges remain differences in the data, an imbalance between the classes, and a limited ability to apply these methods in real clinical settings. Based on the review, this paper suggests more future research on machine learning techniques for detecting pneumonia. In this work, a new system is also introduced to improve both case identification and the clinical diagnosis process. The proposed model was evaluated using the Key Parameter Indicator (KPI) as a feature and was compared with an earlier model. Finally, recommendations are provided for future research on trustworthiness, clinical usefulness, and multi-modal AI systems. 2025 IEEE.
Source
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
convolutional neural networks; Deep learning; machine learning; transfer learning
Coverage
Kumar M., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Shanbhog M., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Alapatt B.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Upreti K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Poonia R.C., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833157981-4;
Format
online
Language
English
Type
Conference paper

Citation

Kumar, Manish; Shanbhog, Manjula; Alapatt, Bosco Paul; Upreti, Kamal; Poonia, Ramesh Chandra, “A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26121.

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