Multi-Task Learning for Lung sound & Lung disease classification

TL;DR

This paper introduces a multi-task deep learning approach for simultaneous lung sound and disease classification, demonstrating high accuracy and potential to assist physicians in diagnosis and patient communication.

Contribution

It proposes a novel multi-task learning framework using four deep learning models, with MobileNet achieving superior performance, and integrates demographic data for COPD risk assessment.

Findings

MobileNet achieved 74% accuracy in lung sound classification.

The approach achieved 91% accuracy in lung disease classification.

Random Forest classifier attained 92% accuracy in COPD risk prediction.

Abstract

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.