Light-CNN Optimization for chest x-ray classification in establishing diagnoses in pneumonia cases

Light-CNN Optimization for Chest X-ray Classification

Abstract

Background: Pneumonia remains a leading cause of mortality worldwide, with chest X-ray serving as the primary diagnostic tool. However, manual interpretation is subject to inter-observer variability, and existing deep learning models often require substantial computational resources that limit deployment in resource-constrained clinical environments.
Objective: This study aimed to develop LightCNN, a novel lightweight convolutional neural network that integrates depthwise separable convolution, inverted residual blocks, channel shuffle mechanism, and lightweight attention for efficient and accurate pneumonia classification from chest X-ray images.
Methods: LightCNN was designed with seven progressive feature extraction stages that incorporate the four aforementioned optimization techniques. The model was trained and evaluated on the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 5,856 images stratified into training (70%), validation (15%), and test (15%) subsets with patient-level splitting to prevent data leakage.
Preprocessing included CLAHE contrast enhancement, normalization, and data augmentation. Training employed the AdamW optimizer with cosine annealing scheduling and class-weighted cross-entropy loss over 50 epochs. The performance of LightCNN was benchmarked against three baseline models — MobileNetV2 (2.23 M parameters), ResNet-18 (11.18 M parameters), and EfficientNet-B0 (4.01 M parameters) — using identical preprocessing and training protocols. Evaluation metrics included accuracy, precision, recall, F1-score, AUC-ROC, parameter count, model size, and inference time.
Results: LightCNN achieved 95.56% accuracy, 0.9556 recall, 0.9584 precision, 0.9562 F1-score, and 0.9875 AUC-ROC on the test set, outperforming all baseline models. The model contains 2.52 million parameters (9.63 MB), representing a 77.4% reduction compared to ResNet-18, with an inference time of 0.25 ms per image — approximately four times faster than the nearest competitor. Ablation study results confirmed that each architectural component contributed incrementally to overall performance; depthwise separable convolution provided the largest efficiency gain, and inverted residual blocks contributed the most substantial accuracy improvement.
Conclusion: LightCNN demonstrates that systematic integration of lightweight architectural techniques can achieve clinically relevant diagnostic performance with minimal computational overhead, supporting its potential deployment in mobile and edge computing scenarios for point-of-care pneumonia diagnosis.