Lung diseases such as pneumonia, tuberculosis, COVID-19, and lung cancer remain significant global health challenges that demand rapid and accurate diagnosis to improve patient outcomes. This study proposes NASNet-ViT, a novel deep learning framework that integrates the powerful convolutional feature extraction of NASNet with the global attention mechanisms of the Vision Transformer (ViT). To enhance diagnostic precision, a multi-stage preprocessing pipeline, termed MixProcessing, is introduced, combining wavelet transform decomposition, adaptive histogram equalization, and morphological filtering to improve image quality and feature clarity. The proposed NASNet-ViT model classifies lung images into five categories, normal, lung cancer, COVID-19, pneumonia, and tuberculosis achieving outstanding performance metrics: 98.9% accuracy, 0.99 sensitivity, 0.988 F1-score, and 0.985 specificity. Compared to established architectures such as MixNet-LD, D-ResNet, MobileNet, and ResNet50, NASNet-ViT demonstrates superior accuracy while maintaining a lightweight model size of only 25.6 MB and fast inference time of 12.4 seconds, making it practical for deployment in real-time, resource-constrained clinical environments. This research advances the field of medical image analysis by offering a robust and scalable AI solution capable of supporting clinicians in timely and precise lung disease diagnosis.
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