Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks

Saumya Borwankar; Jai Prakash Verma; Rachna Jain; Anand Nayyar

doi:10.1007/s11042-022-12958-1

Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks

Multimed Tools Appl. 2022;81(27):39185-39205. doi: 10.1007/s11042-022-12958-1. Epub 2022 Apr 28.

Authors

Saumya Borwankar¹, Jai Prakash Verma¹, Rachna Jain², Anand Nayyar³

Affiliations

¹ Institute of Technology, Nirma University, Ahmedabad, Gujarat India.
² IT department, Bhagwan Parshuram Institute of Technology, New Delhi, India.
³ Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000 Vietnam.

Abstract

Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples. International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset was used for lung sound. Here a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture. The combination of pre-processing steps MFCC, Melspectrogram, and Chroma CENS with CNN improvise the performance of the proposed system, which helps to make an accurate diagnosis of lung sounds. The comparative analysis shows how the proposed approach performs better with previous state-of-the-art research approaches. It also shows that there is no need for a wheeze or a crackle to be present in the lung sound to carry out the classification of respiratory pathologies.

Keywords: CENS (Chroma energy normalized statistics); CNN (Convolutional neural network); MFCC (Mel-frequency cepstral coefficients); Melspectrogram; Respiratory pathologies classification.