3,559 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Respiratory Sound Analysis for the Evidence of Lung Health
Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds
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
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1
A reliable, remote, and continuous real-time respiratory sound monitor with
automated respiratory sound analysis ability is urgently required in many
clinical scenarios-such as in monitoring disease progression of coronavirus
disease 2019-to replace conventional auscultation with a handheld stethoscope.
However, a robust computerized respiratory sound analysis algorithm has not yet
been validated in practical applications. In this study, we developed a lung
sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds
(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,
13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze
labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous
adventitious sound labels (all crackles). We conducted benchmark tests for long
short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM
(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,
CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and
adventitious sound detection. We also conducted a performance comparison
between the LSTM-based and GRU-based models, between unidirectional and
bidirectional models, and between models with and without a CNN. The results
revealed that these models exhibited adequate performance in lung sound
analysis. The GRU-based models outperformed, in terms of F1 scores and areas
under the receiver operating characteristic curves, the LSTM-based models in
most of the defined tasks. Furthermore, all bidirectional models outperformed
their unidirectional counterparts. Finally, the addition of a CNN improved the
accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte
The real time analysis of acoustic weld emissions using neural networks
Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool
in control scenarios where human expertise is so often required. The development of
software emulations and dedicated VLSI devices is proving successful in real world
applications where complex signal analysis, pattern recognition and discrimination are
important factors.
An established observation is that a skilled welder is able to monitor a manual arc
welding process by subconsciously changing the position of the electrode in response to
an adverse change in audible process noise. Expert systems applied to the analysis of
chaotic acoustic emissions have failed to establish any salient information due to the
inabilities of conventional architectures in processing vast quantities of erratic data at real
time speeds.
This paper describes the application of a hybrid ANN system, utilising a combination of
multiple ANN architectures and conventional techniques, to establish system parameter
acoustic signatures for subsequent on line control
ELM and K-nn machine learning in classification of breath sounds signals
The acquisition of Breath sounds (BS) signals from a human respiratory system with an electronic stethoscope, provide and offer prominent information which helps the doctors to diagnosis and classification of pulmonary diseases. Unfortunately, this BS signals with other biological signals have a non-stationary nature according to the variation of the lung volume, and this nature makes it difficult to analyze and classify between several diseases. In this study, we were focused on comparing the ability of the extreme learning machine (ELM) and k-nearest neighbour (K-nn) machine learning algorithms in the classification of adventitious and normal breath sounds. To do so, the empirical mode decomposition (EMD) was used in this work to analyze BS, this method is rarely used in the breath sounds analysis. After the EMD decomposition of the signals into Intrinsic Mode Functions (IMFs), the Hjorth descriptors (Activity) and Permutation Entropy (PE) features were extracted from each IMFs and combined for classification stage. The study has found that the combination of features (activity and PE) yielded an accuracy of 90.71%, 95% using ELM and K-nn respectively in binary classification (normal and abnormal breath sounds), and 83.57%, 86.42% in multiclass classification (five classes)
2D respiratory sound analysis to detect lung abnormalities
In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 hours of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. We also used machine learning algorithms using reliable data to improve Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. Our findings are also contrasted with those of earlier studies. For purposes of generality, we used the MFCC of neutrinos to determine if picture data or raw data produced superior results
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