AI-Powered Pneumonia Detection and Classification: An Extensive Exploration of Deep Learning Technique and Multi-Modal Imaging Techniques

DOI

10.2991/978-94-6239-616-6_30How to use a DOI?

Keywords

Pneumonia; Pneumonia Detection; Deep Learning; 3D-CNN; Medical Imaging; Classification; Diagnosis; Explainable AI; Multi-Modal Integration

Abstract

Pneumonia is a severe respiratory illness that continues to be a major health challenge worldwide, especially impacting infants and older others individuals, patients with weakened immunity. Timely and precise identification is essential to avoid severe complications, yet current diagnostic practices using chest radiographs and CT scans are highly dependent on radiologists, making the process slower and prone to subjective errors. This work proposes an intelligent framework for pneumonia detection, classification, and diagnosis through the utilization of 3D Convolutional Neural networks combined with integrated medical imaging techniques. By analyzing volumetric image data, the proposed system is capable of capturing spatial and depth-related features that improve classification of pneumonia types, including bacterial, viral, and normal conditions. To further enhance reliability, the framework incorporates multi-modal imaging integration along with explainable AI techniques such as heatmap visualization to highlight infected lung regions, ensuring clinical interpretability. The system’s effectiveness will be validated using publicly available datasets and evaluated with evaluation measure including accuracy, precision, recall. Additionally, a supportive interface will be designed for healthcare professionals, enabling faster, trustworthy, and interpretable diagnostic outcomes. This approach aims AI-based solution that improves healthcare decision-making and sets a new direction for medical image analysis in respiratory disease diagnosis.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Puspita Dash
AU  - V. N. Sudharshaan
AU  - B. Manohar Singh
AU  - D. Naveen
PY  - 2026
DA  - 2026/03/31
TI  - AI-Powered Pneumonia Detection and Classification: An Extensive Exploration of Deep Learning Technique and Multi-Modal Imaging Techniques
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 375
EP  - 390
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_30
DO  - 10.2991/978-94-6239-616-6_30
ID  - Dash2026
ER  -