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@article{170734,
        author = {G Aashrith Reddy and G Sree Charan and CH Ankitha and Dr.Geeta Tripathi},
        title = {Innovative Deep Learning Technique for Accurate Lung Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1472-1477},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170734},
        abstract = {The prevalence of lung diseases is rising due to environmental changes, lifestyle shifts, and pollution, especially in low-income countries where air quality is poor. Conditions such as asthma, pneumonia, and chronic obstructive pulmonary disease (COPD) contribute significantly to global mortality. According to WHO, nearly 4 million preventable deaths annually are linked to air pollution. Early detection of lung diseases is crucial to improve treatment outcomes and patient care.Recent advancements in deep learning have transformed the detection and classification of lung diseases using medical imaging, particularly X-rays. Convolutional Neural Networks (CNNs) have shown promise in this field; however, traditional models often lack the precision required for reliable diagnosis. This project proposes an enhanced deep learning framework based on the VGG16 architecture to address these limitations.The model leverages a lung disease dataset sourced from Kaggle, testing both sample and full datasets. The VGG16 architecture outperforms existing techniques in terms of precision, recall, F1 score, and validation accuracy, making it an effective tool for early diagnosis. By assisting clinicians in identifying lung diseases more accurately, this approach enhances the efficiency of medical care and improves patient outcomes. The integration of such advanced models is a significant step toward better disease management and resource allocation in the healthcare sector.},
        keywords = {},
        month = {December},
        }