LightPneumoNet: Lightweight Pneumonia Classifier
Neilansh Chauhan, Piyush Kumar Gupta, Faraz Doja
TL;DR
The paper tackles pneumonia diagnosis from chest X-ray images in resource-limited settings by introducing LightPneumoNet, a from-scratch lightweight CNN with 388k parameters and a 1.48 MB memory footprint. Trained on 5,856 images from the Kermany2018 dataset, the architecture uses four convolutional blocks, grayscale 224×224 inputs, and data augmentation to achieve an independent-test accuracy of approximately 0.942 and a recall of about 0.99, with a precision around 0.92. The study demonstrates that a compact model can match or exceed the performance of larger transfer-learning models while greatly reducing computational demands, enabling deployment on low-cost hardware and in rural clinics. This work supports computer-aided diagnosis as a reliable second-opinion tool in underserved settings, potentially improving patient outcomes through accessible, efficient pneumonia screening.
Abstract
Effective pneumonia diagnosis is often challenged by the difficulty of deploying large, computationally expensive deep learning models in resource-limited settings. This study introduces LightPneumoNet, an efficient, lightweight convolutional neural network (CNN) built from scratch to provide an accessible and accurate diagnostic solution for pneumonia detection from chest X-rays. Our model was trained on a public dataset of 5,856 chest X-ray images. Preprocessing included image resizing to 224x224, grayscale conversion, and pixel normalization, with data augmentation (rotation, zoom, shear) to prevent overfitting. The custom architecture features four blocks of stacked convolutional layers and contains only 388,082 trainable parameters, resulting in a minimal 1.48 MB memory footprint. On the independent test set, our model delivered exceptional performance, achieving an overall accuracy of 0.942, precision of 0.92, and an F1-Score of 0.96. Critically, it obtained a sensitivity (recall) of 0.99, demonstrating a near-perfect ability to identify true pneumonia cases and minimize clinically significant false negatives. Notably, LightPneumoNet achieves this high recall on the same dataset where existing approaches typically require significantly heavier architectures or fail to reach comparable sensitivity levels. The model's efficiency enables deployment on low-cost hardware, making advanced computer-aided diagnosis accessible in underserved clinics and serving as a reliable second-opinion tool to improve patient outcomes.