eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-12-30 18 4 2891 2908 10.13005/bpj/3303 68932 article Himanshu Pant 1 Garima Joshi 2 Bhupesh Rawat 1 Department of Computer Science, Graphic Era Hill University, Bhimtal Campus, India. Department of Zoology, Kumaun University, Nainital, India. Chest X-ray (CXR) imaging is a fundamental diagnostic tool for detecting various thoracic diseases. However, manual interpretation is prone to errors due to subtle variations in disease patterns. This study explores the use of deep learning-based computer-aided detection (CAD) systems to enhance diagnostic accuracy and efficiency in CXR analysis. We assess the efficacy of five deep learning architectures—VGG-16, VGG-19, ResNet, InceptionNet, and CapsNet—on publically accessible CXR datasets that include various disorders. The dataset comprises 3,043 CXR images categorized into four classes: pneumonia (1,345), healthy (1,341), tuberculosis (138), and COVID-19 (217). To address potential dataset challenges, we introduce a novel approach and compare model performance against existing methods. Our results indicate that CapsNet achieves the highest accuracy of 98.48%, surpassing other models based on confusion matrix analysis and key performance metrics. The improved performance of CapsNet is due to its willingness to preserve spatial hierarchies and being resilient to alterations in picture orientation, a frequent drawback of conventional convolutional networks. These findings highlight the potential of CapsNet for improving automated chest disease detection in CXR imaging, demonstrating its viability for clinical applications and further advancements in medical AI research. https://biomedpharmajournal.org/vol18no4/precision-medicine-for-the-lungs-deep-learning-applications-in-thoracic-imaging/ CapsNet Deep Learning Precision Medicine Thoracic Disorders Transfer Learning