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PneumoNet: Deep Neural Network for Advanced Pneumonia Detection.

T R Mahesh¹, Muskan Gupta¹, Abhilasha Thakur²

¹Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, 562112, India.

Current Medical Imaging

|September 24, 2025

Summary

PneumoNet, a novel deep learning model, accurately detects pneumonia from chest X-rays with 98% accuracy. This advancement offers improved diagnostic capabilities for medical imaging and clinical practice.

Keywords:

Chest X-ray analysis Clinical diagnostics.Computational healthcare Convolutional neural networks Machine learning PneumoNet Pneumonia detection

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Computational Pathology

Background:

Chest X-ray analysis for pneumonia detection faces challenges in accuracy and generalizability with current methods.
Classical and early deep learning models exhibit limitations like high false positives and poor performance across diverse datasets.
Accurate pneumonia detection is crucial for timely diagnosis and effective patient management.

Purpose of the Study:

To introduce PneumoNet, a novel deep learning model for enhanced pneumonia detection from chest X-ray images.
To address the limitations of existing methods in accuracy, generalizability, and preprocessing for pneumonia diagnosis.
To improve the diagnostic accuracy and clinical utility of automated pneumonia detection systems.

Main Methods:

Developed PneumoNet, a deep learning architecture utilizing a Convolutional Neural Network (CNN) for feature extraction.
Employed advanced convolutional and pooling layers followed by fully connected layers for intricate feature identification.
Trained and cross-validated PneumoNet on a curated dataset with balanced normal and pneumonia cases.

Main Results:

PneumoNet achieved an overall accuracy of 98% in pneumonia detection.
The model demonstrated high precision (96% normal, 98% pneumonia) and recall (96% normal, 98% pneumonia).
Consistent performance across normal and pneumonia cases highlights the model's reliability.

Conclusions:

PneumoNet shows significant promise for improving pneumonia diagnosis in clinical settings.
The model represents a substantial advancement over current diagnostic methods for chest X-ray analysis.
The findings pave the way for the clinical application of advanced deep learning in medical imaging.