Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-dev...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-developed regions. therefore, computer-aided diagnosis systems are needed. Recently, many classification systems based on deep learning have been proposed. Despite their success, the high development cost for deep networks is still a hurdle for deployment. Deep transfer learning (or simply transfer learning) has the merit of reducing the development cost by borrowing architectures from trained models followed by slight fine-tuning of some layers. Nevertheless, whether deep transfer learning is effective over training from scratch in the medical setting remains a research question for many applications. In this work, we investigate the use of deep transfer learning to classify pneumonia among chest X-ray images. Experimental results demonstrated that, with slight fine-tuning, deep transfer learning brings performance advantage over training from scratch. Three models, ResNet-50, Inception V3 and DensetNet121, were trained separately through transfer learning and from scratch. The former can achieve a 4.1% to 52.5% larger area under the curve (AUC) than those obtained by the latter, suggesting the effectiveness of deep transfer learning for classifying pneumonia in chest X-ray images.
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep r...
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Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool. This review aims to summari...
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Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for plat...
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Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classif...
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This study explored how to inform the design of information systems for medication therapy management, in the context of the southwestern Ontario health system. The data collection comprised document analysis, intervi...
This study explored how to inform the design of information systems for medication therapy management, in the context of the southwestern Ontario health system. The data collection comprised document analysis, interviews, and process mapping, and the analysis of previous interview records. The Functional Resonance Analysis Method (FRAM) was used for data analysis to identify the effects of functional variability in system outcomes, and to propose ways to redesign future systems. The results from the FRAM showed that shortcomings in the information systems require users to adapt in many ways. While these adjustments are essential to delivering care in everyday practice, they may lose their effectiveness in the face of specific situations, creating brittleness and risk of adverse outcomes as users are led to make challenging decisions.
Commercial myoelectric prostheses need to be accurate and clinically viable to be successful. This study proposed a simultaneous and proportional control scheme with frequency division technique (SPEC-FDT) to address ...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Commercial myoelectric prostheses need to be accurate and clinically viable to be successful. This study proposed a simultaneous and proportional control scheme with frequency division technique (SPEC-FDT) to address limitations in current myoelectric prosthesis control, specifically to address non-stationaries such as contraction level variations and unintended activations. Twenty able-bodied participants (14 males and 6 females, age 23.4 ± 3.0) and four individuals with transradial amputations performed wrist movements (flexion/extension, rotations and combined movements) in two degrees-of freedom virtual tasks. The SPEC-FDT had a completion rate (CR)>90% for both control and clinical participants which was significantly higher than the conventional technique (CR=68%). Our results showed that SPEC-FDT is highly accurate for both able-bodied and clinical participants and provides a robust myoelectric control scheme allowing for increased prosthetic hand functions.
作者:
Petteri HyvärinenMichal FereczkowskiEwen N. MacDonalda Acoustics Lab
Department of Signal Processing and Acoustics Aalto University Espoo Finlandb Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark b Hearing Systems Section
Department of Health Technology Technical University of Denmark Lyngby Denmarkc Institute of Clinical Research Faculty of Health Sciences University of Southern Denmark Odense Denmarkd Research Unit for ORL – Head & Neck Surgery and Audiology Odense University Hospital & University of Southern Denmark Odense Denmark b Hearing Systems Section
Department of Health Technology Technical University of Denmark Lyngby Denmarke Department of Systems Design Engineering University of Waterloo Waterloo Canada
ObjectiveThe aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the *** studi...
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ObjectiveThe aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the *** studies were conducted: Study 1 was a notched-noise masking experiment with three different setups: in a psychoacoustic test booth with a standard laboratory PC; in a psychoacoustic test booth with a mobile device; and in a quiet office room with a mobile device. Study 2 employed the same task as Study 1, but compared circumaural headphones to insert *** sampleNine and ten young, normal-hearing participants completed studies 1 and 2, *** test-retest accuracy of the notched-noise test on the mobile implementation did not differ from that for the laboratory setup. A possible effect of the earphone design was identified in Study 1, which was corroborated by Study 2, where test-retest variability was smallest when comparing results from experiments conducted using identical acoustic *** and test-retest repeatability comparable to standard laboratory settings for the notched-noise test can be obtained with mobile equipment outside the laboratory.
Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of scre...
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This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance i...
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This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.Abbreviations:AUC: Area under curve; CAD: Computer aided system; CC: Cranio caudal; CNN: Convolutional neural network; DNN: Deep neural network; DDSM: Digital Database for Screening Mammography; DM: Digital mammography; DL: Deep learning; EDSR: Enhanced Deep Residual Network; E2E: End to End; ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks; ESPCN: Efficient sub-pixel convolutional neural network; GAN: Generative adversarial network; HR: High resolution; IoU: Intersection over Union; LR: Low resolution; MDSR: Multi-scale deep super-resolution; MLO: Mediolateral Oblique; PSNR: Peak signal to Noise Ratio; RoI: Region of interest; RDN: Residua Dense Network; RDB: Residual Dense Block; RNN: Recurrent Neural Network; ReLU: Rectified Linear Unit; SR-GAN: Super-Resolution Using a Generative Adversarial Network; SSIM: Structural Sim
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