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@article{174004,
        author = {Prof. Shah Saloni Niranjan and Pawar Rohit Balaso and Raut Aditi Shivaji and Jadhav Sneha Nitin},
        title = {Survey Paper On “Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM”},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2155-2157},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174004},
        abstract = {In this project, we developed an easy-to-use and affordable algorithm to analyze respiratory sounds, which can be used on any device. The goal was to classify different types of breathing sounds using machine learning techniques. We used two types of features to represent the sounds: Gammatone Cepstrum Coefficients (GTCC) and Short-Time Fourier Coefficients (STFC). These features help the system understand the characteristics of the sounds. The algorithm then uses a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network to classify the sounds accurately. We created four datasets to train and test the algorithm. These datasets include: Healthy versus pathological sounds (to distinguish between normal and abnormal breathing), Classification of different types of sounds, like rales, rhonchi, and normal breath sounds, Classification of individual types of respiratory sounds, and A complete classification that includes all types of breathing sounds. The algorithm is designed to be simple, cost-effective, and can work on various devices, making it accessible for a wide range of users, including healthcare professionals, researchers, or anyone interested in analyzing respiratory sounds.},
        keywords = {Respiratory Sound Analysis, Breathing Sound Classification, Gammatone Cepstrum Coefficients (GTCC), Short-Time Fourier Coefficients (STFC), CNN (Convolutional Neural Networks) LSTM (Long Short-Term Memory),Machine Learning, Healthcare Applications},
        month = {March},
        }