Hybrid MobileNetV2–vision transformer approach for multi-label classification of chest X-rays

Abstract

The precise identification of thoracic disorders using chest X-rays (CXRs) is essential for favorable clinical diagnosis. However, it continues to be difficult because of inter-class similarities and overlapping symptoms. This study introduces a hybrid MobileNetV2–Vision Transformer (ViT) framework for multi-label classification of CXR images into 14 disease categories on NIH CXR14 dataset. MobileNetV2 serves as a lightweight feature extractor to obtain distinctive spatial representations that are tokenized and enhanced with positional encodings. The encoded features were processed via transformer layers, wherein self-attention mechanisms captured long-range interdependence and global contextual relationships. An output layer based on the sigmoid function facilitated the concurrent prediction of various illness labels. The novelty of the proposed work lies in the PSO-tuned hybrid architecture, which adaptively optimizes key hyperparameters of the MobileNetV2–ViT framework to achieve improved accuracy, faster convergence, and enhanced computational efficiency compared to conventional approaches. This implementation uses an economical, GPU-accelerated instance tailored for inference workloads. It is equipped with a single NVIDIA A10G GPU with 24 GB of dedicated GPU memory, and the proposed work is carried out on AWS SageMaker. The experimental assessment employing five-fold cross-validation showed strong performance, with an average accuracy of 94.6%, precision of 88.1%, recall of 85.9%, specificity of 94.17%, and F1-score of 91.1%. Grad-CAM representations increased interpretability by identifying disease-relevant areas. The results underscore the efficacy of the proposed hybrid technique as a dependable instrument for computer-aided diagnosis of thoracic illnesses.