Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

Abstract

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

The Impact of Leadership Styles on Employees Productivity in Organizations: A Comparative Study Among Leadership Styles

“…The style depends on how ready the followers are [57]. THE type of telling expresses a strong degree of commitment to duties and low regard for individuals and ties, and this extremely directive style gives clear guidance about how to execute the tasks [58]. Are both the individuals and the activities the selling type focused on a high degree of interest.…”

Harvesting social media sentiment analysis to enhance stock market prediction using deep learning

“…Adopting the SVM also defines tweets as positives, negative, or neutral sentiments, resulting in increased predictive ability on the SM. Many efforts have been made (Sushree et al, 2018;Ghayvat et al, 2019;Ghayvat, Pandya & Awais, 2021;Srivastava et al, 2021) for SM prediction; predicting the firm's effective SM price with Twitter data streaming shows the collection in actual data time. The research reveals how SA of public mood extracted from Twitter or any social media feed can effectively predict movements in independent share prices.…”

An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

“…For unbalanced classification problems, the area under the curve (AUC) and G-mean are usually chosen as the key performance indexes. Sensitivity, which also called the true positive rate, measures the proportion of positives that are correctly identified [28]. The purpose of the models is to improve malicious URLs detection, we also evaluated sensitivity.…”