COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds.
Lella Kranthi KumarP J A AlphonsePublished in: The European physical journal. Special topics (2022)
In the last 2 years, medical researchers and clinical scientists have paid close attention to the problem of respiratory sound classification to classify COVID-19 disease symptoms. In the physical world, very few AI-based (Artificial Intelligence) techniques are often used to detect COVID-19/SARS-CoV-2 respiratory disease symptoms from the human respiratory system-generated acoustic sounds such as acoustic voice sound, breathing (inhale and exhale) sounds, and cough sound. We propose a light-weight Convolutional Neural Network (CNN) with Modified-Mel-frequency Cepstral Coefficient (M-MFCC) using different depths and kernel sizes to classify COVID-19 and other respiratory sound disease symptoms such as Asthma, Pertussis, and Bronchitis. The proposed network outperforms conventional feature extraction models and existing Deep Learning (DL) models for COVID-19/SARS-CoV-2 classification accuracy in the range of 4-10%. The model's performance is compared with the COVID-19 crowdsourced benchmark dataset and gives a competitive performance. We applied different receptive fields and depths in the proposed model to get different contextual information that should aid in classification. And our experiments suggested 1 × 12 receptive fields and a depth of 5-Layer for the light-weight CNN to extract and identify the features from respiratory sound data. The model is also trained and tested with different modalities of data to showcase its effectiveness in classification.
Keyphrases
- sars cov
- deep learning
- convolutional neural network
- artificial intelligence
- coronavirus disease
- machine learning
- respiratory syndrome coronavirus
- big data
- endothelial cells
- body mass index
- weight loss
- physical activity
- healthcare
- randomized controlled trial
- chronic obstructive pulmonary disease
- electronic health record
- mental health
- magnetic resonance imaging
- sleep quality
- magnetic resonance
- weight gain
- lung function
- social media
- data analysis