Detection of abnormalities in imaged lung sounds based on deep learning
Authors: Matsumoto Shogo , Wakabayashi Naoya , Shimakawa Hiromitsu , Harada Humiko 
Published: July 2023

Despite the increase in respiratory diseases, the number of respiratory
specialists is decreasing. The shortage of respiratory specialists has made
the COVID-19 pandemic more serious. The pandemic has revealed the difficulty
of controlling transmission, diagnosing, monitoring disease status, and
responding to symptoms of infectious respiratory diseases. The global
outbreak of the new virus infections has reminded us of the fragility of the
conventional healthcare system.The most effective examination in the
examination of respiratory disease is auscultation. However, features of
abnormal sounds the disease brings are too obscure for doctors who are not
specialists in respiratory to distinguish abnormal sounds from normal ones.
Furthermore, due to aging, we would suffer from difficulty in hearing
high-pitched sounds, which obliges even specialists often make mistakes in
diagnosis. Diagnosis by auscultation depends on subjective judgment and the
skill of the specialist. Today, when specialists are in short supply,
information technology is expected to support even non-specialists to be
able to diagnose respiratory diseases with high accuracy based on objective
criteria. Utilizing the technologies, we should prepare for new
pandemics.Specialists diagnose respiratory diseases by listening for
peculiar sounds from the auscultatory sounds of patients who are suffering
from lung disease. The study proposed in the paper transforms lung sounds
collected by auscultation into a spectral image using the short-time Fourier
transform. If auscultatory sounds contain disease-specific sounds, specific
features should also appear in the spectral image of lung sounds. Deep
learning techniques for analyzing images have made remarkable
progress.Images can provide objective judgment criteria even to
non-specialists. Analysis of images allows both specialists and
non-specialists to diagnose objectively, unaffected by hearing loss due to
aging. Doctors have accountability for patients on diseases. Images have
comprehensive explanatory power for patients.Only a short-time Fourier
transform of the spectral image of auscultatory sounds does not sufficiently
highlight features specific to respiratory disease. The proposed method
converts auscultatory sounds from the lung into a spectral image that
emphasizes the frequency region of the sound recognizable to humans. The
study refers to it as a mel-spectrogram, which facilitates finding disease
features. The proposed method detects disease-specific features appearing in
mel-spectrograms with Yolo, an object detection technique based on deep
learning. The proposed method has discriminated auscultatory sounds obtained
from actual patients with an accuracy of 0.7 in the F1-Score.Deep learning
analysis of images provides evaluation criteria that are objective and
independent of the skill of doctors. This study will enable non-specialists
in respiratory medicine to examine whether persons are suffering from
respiratory diseases, which would eliminate the shortage of specialists.
This is diagnostic support for nonspecialists to address the explosion of
patients due to respiratory infection outbreaks in the pandemic. It
contributes to preventing the collapse of health care.