Rhonchi are low-pitched, continuous waves of noises with frequencies under 200 Hz that resemble snoring (Naves et al.

They typically develop as a result of tracheal or laryngeal stenosis. Similar to this, stridor noises are high-pitched waves lasting longer than 250 ms and exceeding 500 Hz. These noises are caused by bronchial tube irritation or constriction. Breathing whistle-like high-pitched continuous waves lasting longer than 80 ms are referred to as wheezes (Naves et al.

Many different types of anomalous (adventitious) lung sounds, such as wheezes, stridor, rhonchi, and crackles, superimpose regular sounds. An irregularity in the auscultated sound typically denotes lung fluid, infection, inflammation, or blockage (Chang and Lai 2010 Sengupta et al. The sound of the lungs can be normal or abnormal. Auscultation of the lung using a stethoscope is the traditional method and one of the widely used diagnostic method which is regularly used by specialists and general practitioners for performing the initial realization of the respiratory system condition. Like other disease especially the serious one, prevention is the key for decreasing it affect, diagnosis and treatment in early stages are considered key factors and methods for limiting the negative impact of these deadly diseases. These lung diseases are affecting the overall healthcare system severely while on the other hand, they accordingly affect the general population's lives. These diseases are responsible for the death of more than 3 million people each year worldwide (Chang and Cheng 2008 Chang and Lai 2010). Based on the World Health Organization (WHO) statistics, the main five lung diseases (Moussavi 2006) are: tuberculosis, lung cancer, chronic obstructive pulmonary disease (COPD), asthma, and acute lower respiratory tract infection (LRTI). In recent years, lung diseases became the third largest cause of death globally (Lehrer 2018). Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. Also, we will find out the best deep learning model for this task. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases.

Rhonchi lung sounds audio