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@article{201671,
        author = {ANKITA AGARWAL and MAHENDRA SINGH and SOVAN MOHANTY},
        title = {AI-Driven Lung Disease Detection: A Review of Deep Learning Models, Multimodal Integration, and Resource-Efficient Approaches.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4267-4271},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201671},
        abstract = {AI (artificial intelligence) has become a powerful tool in medical science, especially in the case of the detection of lung diseases like COVID-19, pneumonia, ILD, tuberculosis, etc. This review paper will focus your mind on learning the CNN based advance Deep Learning Models, which will help you to detect the diseases, predict their progression, using CT scans and X-ray images. These advanced models are multimodal; they include clinical information and laboratory information. Deep learning models achieve high accuracy in classifying diseases and show results after combining imaging data with clinical information. Lightweight model reduces the computational cost and can be used anywhere in areas where there are limited resources. Future directions include the development of a model trained on large datasets and heterogeneous datasets, which means different types of diseases, which will later be segregated. Overall, the paper explains the current state and future potential of AI approaches in lung diseases.},
        keywords = {Deep Learning, Convolutional Neural Network (CNN), Lung Disease Detection, Medical Image Analysis, Multimodal Learning, Lightweight CNN, Healthcare I.},
        month = {May},
        }