Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information

TL;DR

This paper presents a deep learning framework combining auditory and demographic data for improved pulmonary disease diagnosis, demonstrating increased accuracy and potential for portable healthcare solutions.

Contribution

It introduces a multi-modal neural network approach that integrates auditory and demographic information, enhancing diagnostic accuracy for pulmonary diseases.

Findings

Accuracy increased by 5% with combined data

Demographic info can be estimated via computer vision

Deployment on NVIDIA TX2 shows feasibility for portable systems

Abstract

Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.