IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 149 10.51583/IJLTEMAS.2026.150700139 Artificial Intelligence PulmoScan AI: An Explainable Deep Learning-Based Clinical Decision Support System for Multi-Class Lung Disease Detection Using Chest X-Ray Images Jacob Varghese Joel Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology, Nagpur India Choudhary Manchit Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology, Nagpur India Sara Bilu Manna Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology, Nagpur India Cholangi Navin Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology, Nagpur India Yogesh Golhar Dr. Assistant Professor, Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology, Nagpur India 15 7 1818 1832 12 08 2026 17 08 2026 24 08 2026 Deep learning chest X-ray lung disease detection EfficientNetB0 transfer learning Grad-CAM explainable AI computer-aided diagnosis pneumonia tuberculosis clinical decision support Abstract

Lung diseases such as pneumonia and tuberculosis (TB) represent a major global health burden, particularly in low- and middle-income regions with limited access to expert radiological interpretation. Early and accurate diagnosis through chest X-ray (CXR) imaging is critical, yet conventional radiological interpretation suffers from inter-observer variability and a shortage of expert radiologists in resource-limited settings. This paper proposes PulmoScan AI, a deep learning-based full-stack clinical decision support system for automated detection and classification of lung diseases from CXR images. The system employs EfficientNetB0 with transfer learning for multi-class classification, trained on a curated dataset of 11,910 images, and detects three categories: Normal, Pneumonia, and Tuberculosis. The model achieves a test accuracy of 97.9%, AUC-ROC of 0.9982, macro precision of 98.28%, macro recall of 97.70%, and macro F1-score of 97.96%, outperforming four baseline architectures (a shallow CNN, a custom CNN, MobileNetV2, and ResNet50) trained under an identical protocol; five-fold cross-validation confirms stable performance across data partitions (97.20 ± 0.39% mean accuracy). A web-based clinical decision support interface integrates real-time prediction with confidence thresholding, Grad-CAM explainability, and an occupational risk assessment module. Experimental results demonstrate the feasibility and clinical potential of the approach for deployment in health screening programs.

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