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@article{193674,
        author = {Avani Naik and Gangambika Deshmukh and Nitya Bagali and Poornima. C and Prof. Sangameshwar Kawdi},
        title = {Classification and Recognition of Lung Sounds Using Improved Bi- ResNet Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1382-1389},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193674},
        abstract = {In order to help in the early detection of respiratory conditions like COPD, bronchitis, and asthma, this research offers a sophisticated deep learning framework for lung sound classification. The system combines an enhanced Bi-ResNet architecture that uses skip connections and residual blocks for deeper feature learning with the Short-Time Fourier Transform (STFT) and Wavelet Transform for feature extraction. The model outperforms conventional techniques in terms of accuracy by utilizing data augmentation and feature fusion. With an F1 score of 71.05%, experimental findings on the ICBHI 2017 dataset show a classification accuracy of 77.81%, which is 25.02% better than the baseline Bi ResNet. The potential of AI-driven diagnostic tools to lower subjectivity in medical practice and offer scalable respiratory healthcare solutions is highlighted in this paper.},
        keywords = {Lung sound classification, Deep learning, STFT and Wavelet features, Improved Bi-ResNet, Respiratory diagnostics},
        month = {March},
        }