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@article{162757,
        author = {Pruthvika M and Sujatha S and Arshiya Sulthana T and Divya Shree S and Vasumathi R},
        title = {Pulmonary Ailment Classification using Phonopneumography},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {955-958},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162757},
        abstract = {: Lung auscultation is one of the most popular diagnostic modalities used by the pulmonary experts to analyze the condition of the respiratory system. When auscultating various areas on the anterior and posterior sides of the chest, lung sounds can be detected. Lung sounds are indicative of different anatomical flaws in the lungs and provide accurate prognoses regarding respiratory health, resulting in more trustworthy medical tool for identifying respiratory disorders. According to a recent study conducted by the world health organization (WHO), approximately ten million (M) people die each year as a result of respiratory diseases. In order to analyze respiratory sounds on a computer, we developed a cost-effective and easy-to-use Algorithm that can be used with any device. Employed two types of machine learning algorithms; Gammatone Cepstrum Coefficients Features in a Convolutional Neural Network and Since using GTCC and STFC features with a CNN-LSTM algorithm. We prepared four data sets for CNN-LSTM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. },
        keywords = {Respiratory Disease classification, Pulmonary disease classification, Lung disease classification, Lung disease classification based on lung sounds.},
        month = {},
        }