Lung anomaly detection from respiratory sound database (sound signals).

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy person's sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. With constant rise in the COVID-19 cases, there has been a constant rise in the need of efficient and safe ways to detect an infected individual. With the cases multiplying constantly, the current detecting devices like RT-PCR and fast testing kits have become short in supply. An effectual Covid-19 detection model using devised hybrid Honey Badger Optimization-based Deep Neuro Fuzzy Network (HBO-DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed hybrid HBO algorithm. Accordingly, the developed Hybrid HBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed Hybrid HBO-based DNFN is outpaced than other existing approaches in terms of testing accuracy, sensitivity and specificity of "0.9176, 0.9218 and 0. 9219". All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. When k-fold value is 9, sensitivity of existing techniques and developed JHBO-based DNFN is 0.8982, 0.8816, 0.8938, and 0.9207. The sensitivity of developed approach is improved by means of gaussian filtering model. The specificity of DCNN is 0.9125, BI-AT-GRU is 0.8926, and XGBoost is 0.9014, while developed JHBO-based DNFN is 0.9219 in k-fold value 9.