  Respiratory disease, the third leading cause of deaths globally, is
considered a high-priority ailment requiring significant research on
identification and treatment. Stethoscope-recorded lung sounds and artificial
intelligence-powered devices have been used to identify lung disorders and aid
specialists in making accurate diagnoses. In this study, audio-spectrogram
vision transformer (AS-ViT), a new approach for identifying abnormal
respiration sounds, was developed. The sounds of the lungs are converted into
visual representations called spectrograms using a technique called short-time
Fourier transform (STFT). These images are then analyzed using a model called
vision transformer to identify different types of respiratory sounds. The
classification was carried out using the ICBHI 2017 database, which includes
various types of lung sounds with different frequencies, noise levels, and
backgrounds. The proposed AS-ViT method was evaluated using three metrics and
achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20
split ratio in terms of unweighted average recall and overall scores
respectively for respiratory sound detection, surpassing previous
state-of-the-art results.
