Published Paper Details:

: Respiratory disease, any of the diseases and disorders of the airways and the lungs that affect human respiration. Diseases of the respiratory system may affect any of the structures and organs that have to do with breathing, including the nasal cavities, the pharynx (or throat), the larynx, the trachea (or windpipe), the bronchi and bronchioles, the tissues of the lungs, and the respiratory muscles of the chest cage and also most affected organ in the body by COVID-19 is lungs which damages the alveoli (tiny air sacks). This paper presents the detection of respiratory diseases based on breathing sound parameter by using deep learning. The objective of paper is state deep learning-based lungs disease detection model. taking consideration of recent work and future work we are here collect sound data of different respiratory disease from online repository i.e., URTI, LRTI, Bronchiectasis, Bronchiolitis and COPD and also have healthy patients sound records. In methods and procedure, we are using MFCC concept for sound feature extraction Here we will be using Mel-Frequency Cepstral Coefficients (MFCC) from the audio samples. The MFCC summarizes the frequency distribution across the window size, so it is possible to analyze both the frequency and time characteristics of the sound. These audio representations will allow us to identify features for classification. The presented information can be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.