DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES

Pneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods

COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis

At the time of writing, the world population is suffering from more than 10,000 registered COVID-19 disease epidemic induced deaths since the outbreak of the Corona virus more than three months ago now officially known as SARS-CoV-2. Since, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient wellbeing. We quickly guide further through challenges that need to be faced for real-life usage. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

A framework for automated heart and lung sound analysis using a mobile telemedicine platform

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 246-261).Many resource-poor communities across the globe lack access to quality healthcare,due to shortages in medical expertise and poor availability of medical diagnostic devices. In recent years, mobile phones have become increasingly complex and ubiquitous. These devices present a tremendous opportunity to provide low-cost diagnostics to under-served populations and to connect non-experts with experts. This thesis explores the capture of cardiac and respiratory sounds on a mobile phone for analysis, with the long-term aim of developing intelligent algorithms for the detection of heart and respiratory-related problems. Using standard labeled databases, existing and novel algorithms are developed to analyze cardiac and respiratory audio data. In order to assess the algorithms' performance under field conditions, a low-cost stethoscope attachment is constructed and data is collected using a mobile phone. Finally, a telemedicine infrastructure and work-flow is described, in which these algorithms can be deployed and trained in a large-scale deployment.by Katherine L. Kuan.M.Eng

Human-centred artificial intelligence for mobile health sensing:challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

Multimedia sensors embedded in smartphones for ambient assisted living and e-health

The final publication is available at link.springer.com[EN] Nowadays, it is widely extended the use of smartphones to make human life more comfortable. Moreover, there is a special interest on Ambient Assisted Living (AAL) and e-Health applications. The sensor technology is growing and amount of embedded sensors in the smartphones can be very useful for AAL and e-Health. While some sensors like the accelerometer, gyroscope or light sensor are very used in applications such as motion detection or light meter, there are other ones, like the microphone and camera which can be used as multimedia sensors. This paper reviews the published papers focused on showing proposals, designs and deployments of that make use of multimedia sensors for AAL and e-health. We have classified them as a function of their main use. They are the sound gathered by the microphone and image recorded by the camera. 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