Advanced Artificial Intelligence for Adventitious Lung Sound Detection

This research initiative by Suraj Vathsa focuses on using transfer learning and hybridization techniques to detect adventitious lung sounds such as wheezes and crackles from patient lung sound recordings. By developing an AI system that combines deep learning models and generative modeling for data augmentation, the study aims to enhance respiratory disease diagnosis through automated sound analysis. Hybridization of CNN, LSTM, and VAE models showed improved performance in crackle and wheeze detection, addressing challenges of data quality and the lack of patient information in existing datasets.

• Transfer learning
• Hybridization
• Deep Learning
• Respiratory Disease
• Artificial Intelligence

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1. Transfer Learning and Hybridization for adventitious lung sound detection Internet Research Initiative 2020-2021 Suraj Vathsa

2. Ideas Goal Detect adventitious lung sounds as a first step in autonomous respiratory disease diagnosis. Develop an Artificial Intelligence (Deep Learning) enabled system to identify adventitious sounds such as wheezes and crackles in audio recordings of patient lung sounds. Employ transfer learning, i.e., using an existing deep learning model trained for a similar task. Employ hybridization of two types of deep learning models to incorporate spectral and temporal features that can be generated from lung sounds. Data imputation using generative modelling to augment training data Findings/Conclusions/Next Steps Research Method/Process ICBHI 2017 Dataset provides a total of 5.5 hours of recordings containing 6898 respiratory cycles. Hybridization was useful in both the tasks of crackle and wheeze detection while transfer learning helped in boosting the performance in crackle detection. Previous works train simple CNN or simple RNN from scratch. Trained VGGish, a popular audio classification model hybridized with a recurrent neural network for the task of detecting crackles and wheezes. Downstream diagnosis was still unsatisfactory since various key features such as patient medical history and geography were missing from the dataset. Lack of quality data was a major challenge. Addressed using audio manipulation tricks and generative modelling. Future work includes building a comprehensive dataset which incorporates lung sounds, patient history, patient demographics, and environmental variables of patient s location. Further developing generative modelling to help with effective data imputation. Convolutional Variational Autoencoder employed for generative modelling.

3. Dataset ICBHI 2017 Challenge Preprocessing Respiratory cycle centric Repetition of respiratory cycles Audio augmentation Features extraction from raw audio (mel spectrograms and MFCCs) Audio Generation Generated Wheeze Natural Wheeze

4. Deep Learning Models Used VGGish + LSTM CNN + LSTM Convolutional VAE

5. Results Hybridization (CNN + LSTM and VGGish + LSTM) outperforms simple deep learning models Transfer Learning helps in crackle detection. TSNE plot