PneuMatrix: An Advanced Deep Learning based Approach for Precise Prediction of Lung Diseases from Chest X-Rays

Lung diseases, including pneumonia, fibrosis, consolidation, emphysema, and effusion, are leading causes of global mortality and significantly strain healthcare systems, particularly in underserved regions. Early and precise diagnosis is essential to mitigate complications and enhance patient outcomes. The main diagnostic procedure for identifying thoracic abnormalities is chest X-rays (CXR); however, manual interpretation is often subjective, time-consuming, and prone to inter-observer variability. Consequently, leveraging automated diagnostic systems based on deep learning methodologies has become a focal point in medical imaging research. A new deep learning framework for classifying lung diseases into many classes using CXR pictures is presented in this paper. The methodology incorporates advanced preprocessing techniques such as data augmentation, contrast enhancement, and noise reduction to address class imbalance and improve model robustness. A custom convolutional neural network (CNN) architecture was developed, featuring optimized convolutional layers, max-pooling, and dropout layers to enrich feature extraction and reduce overfitting. To evaluate the model’s effectiveness in-depth, several metrics were employed, including recall, accuracy, F1-score, and precision. Experiments demonstrate the model’s effectiveness in accurately classifying multiple lung diseases, achieving high precision and recall across diverse disease categories. By utilizing state-of-the-art tools, including TensorFlow and Keras, the framework ensures scalability and real-world applicability. The suggested work underscores the capacity of deep learning-driven diagnostic routines to improve clinical workflows, alleviate the burden on radiologists, and provide equitable healthcare access, particularly in low-resource settings. This study contributes to advancing automated diagnostic tools in medical imaging, aiming to facilitate early detection and effective management of lung diseases.