A Framework for Detecting Pulmonary Diseases from Lung Sound Signals Using a Hybrid Multi-Task Autoencoder-SVM Model
Khwanjit Orkweha,
Khomdet Phapatanaburi,
Wongsathon Pathonsuwan,
Talit Jumphoo,
Atcharawan Rattanasak,
Patikorn Anchuen,
Watcharakorn Pinthurat,
Monthippa Uthansakul,
Peerapong Uthansakul
Affiliations
Khwanjit Orkweha
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Khomdet Phapatanaburi
Department of Telecommunication Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan (RMUTI), Nakhon Ratchasima 30000, Thailand
Wongsathon Pathonsuwan
Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Talit Jumphoo
Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Atcharawan Rattanasak
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Patikorn Anchuen
Navaminda Kasatriyadhiraj Royal Air Force Academy, Saraburi 18180, Thailand
Watcharakorn Pinthurat
Department of Electrical Engineering, Rajamangala University of Technology Tawan-Ok, Chanthaburi 22210, Thailand
Monthippa Uthansakul
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Peerapong Uthansakul
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving feature extraction. However, researchers have not thoroughly investigated the study of lung sounds (LSs) for pulmonary disease detection. This paper introduces a new framework that utilizes an MTAE model to detect lung diseases based on LS signals. The model integrates an autoencoder and a supervised classifier, simultaneously optimizing both classification accuracy and signal reconstruction. Furthermore, we propose a hybrid approach that combines an MTAE and a Support Vector Machine (MTAE-SVM) to enhance performance. We evaluated our model using LS signals from a publicly available database from King Abdullah University Hospital. The model attained an accuracy of 89.47% for four classes (normal, pneumonia, asthma, and chronic obstructive pulmonary disease) and 90.22% for three classes (normal, pneumonia, and asthma cases). Using the MTAE-SVM, the accuracy was further improved to 91.49% for four classes and 93.08% for three classes, respectively. The results indicate that the MTAE and MTAE-SVM have a considerable potential for detecting pulmonary diseases from lung sound signals. This could aid in the creation of more user-friendly and effective diagnostic tools.
