Hybrid Separable Conv-ViT–CheXNet with Explainable Localization for Pneumonia Diagnosis

Abstract

This research presents a robust, interpretable, and computationally efficient deep learning framework for multiclass pneumonia classification from chest X-ray images, with a strong emphasis on diagnostic accuracy, model transparency, and real-time applicability in clinical settings. We propose SCViT-CheXNet, a novel hybrid architecture that integrates a Separable Convolution Vision Transformer (SCViT) with a simplified CheXNet backbone based on DenseNet121 to achieve efficient spatial feature extraction, hierarchical representation learning, and faster model convergence. The use of separable convolution significantly reduces computational complexity while preserving discriminative feature learning, and the transformer module effectively captures long-range dependencies in radiographic patterns. To address the critical issue of class imbalance inherent in medical imaging datasets, an Auxiliary Classifier Deep Convolutional Generative Adversarial Network (ADCGAN) is employed to generate synthetic samples for underrepresented pneumonia categories, thereby enhancing data diversity and improving model generalization. The proposed framework is extensively evaluated on two benchmark datasets: Dataset-1, consisting of Normal, Viral, Bacterial, and Fungal Pneumonia cases, and Dataset-2, comprising Normal, Viral Pneumonia, COVID-19, and Lung Opacity classes. Model interpretability is ensured through Gradient-weighted Class Activation Mapping (Grad-CAM), which enables visualization of disease-specific regions in chest X-ray images and validates the clinical relevance of the learned representations. Experimental results demonstrate that SCViT-CheXNet consistently outperforms existing convolutional neural network and transformer-based approaches, achieving 99% accuracy, precision, recall, and F1-score across both datasets. The synergistic integration of separable convolution, transformer-based feature modeling, and GAN-driven data augmentation results in a lightweight yet highly accurate and interpretable diagnostic system. Overall, the SCViT-CheXNet framework shows strong potential for deployment in automated pneumonia and COVID-19 screening systems, offering reliable support for real-time clinical decision-making and contributing to improved patient outcomes.