Multi-Task Learning for Lung sound & Lung disease classification
Suma K V Deepali Koppad Preethi Kumar Neha A Kantikar Surabhi Ramesh
In recent years, advancements in deep learning techniques have considerably
enhanced the efficiency and accuracy of medical diagnostics. In this work, a
novel approach using multi-task learning (MTL) for the simultaneous
classification of lung sounds and lung diseases is proposed. Our proposed model
leverages MTL with four different deep learning models such as 2D CNN,
ResNet50, MobileNet and Densenet to extract relevant features from the lung
sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the
current study. The MTL for MobileNet model performed better than the other
models considered, with an accuracy of74\% for lung sound analysis and 91\% for
lung diseases classification. Results of the experimentation demonstrate the
efficacy of our approach in classifying both lung sounds and lung diseases
concurrently.
  In this study,using the demographic data of the patients from the database,
risk level computation for Chronic Obstructive Pulmonary Disease is also
carried out. For this computation, three machine learning algorithms namely
Logistic Regression, SVM and Random Forest classifierswere employed. Among
these ML algorithms, the Random Forest classifier had the highest accuracy of
92\%.This work helps in considerably reducing the physician's burden of not
just diagnosing the pathology but also effectively communicating to the patient
about the possible causes or outcomes.