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@article{174873,
        author = {M. SOPHIYA SUGANTHA GRACE and SANJAY C and SUGAPRIYAN V and HARISH V},
        title = {MULTI LABEL CLASSIFICATION OF LUNG DISEASES USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1146-1152},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174873},
        abstract = {This project aims to address the pressing need for reliable and early detection of lung-related illnesses, including COPD, COVID-19, pneumonia, and lung cancer. A Region-based Convolutional Neural Network (RCNN) model was designed using MATLAB, utilizing chest X-ray images to classify these conditions. One of the standout features of this study is the incorporation of RCNN for generating synthetic images, which effectively tackles data imbalance issues and enhances model stability. This innovative approach significantly improved classification accuracy when compared to traditional methods.The system leverages MATLAB’s deep learning toolboxes for model training, validation, and performance analysis. Experimental outcomes reveal the model’s strong capability in distinguishing between different lung conditions, demonstrating the potential of AI-powered medical imaging to support clinical decision-making. By facilitating early and precise diagnosis, this research highlights how RCNN can enhance diagnostic accuracy, ultimately contributing to improved patient care and medical advancements.},
        keywords = {},
        month = {April},
        }