41,847 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
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region
A dual-wavelength thulium-doped fluoride
fiber (TDFF) laser is presented. The generation of the TDFF
laser is achieved with the incorporation of a single modemultimode-
single mode (SMS) interferometer in the laser
cavity. The simple SMS interferometer is fabricated using the
combination of two-mode step index fiber and single-mode fiber.
With this proposed design, as many as eight stable laser lines
are experimentally demonstrated. Moreover, when a tunable
bandpass filter is inserted in the laser cavity, a dual-wavelength
TDFF laser can be achieved in a 1.5-μm region. By heterodyning
the dual-wavelength laser, simulation results suggest that the
generated microwave signals can be tuned from 105.678 to
106.524 GHz with a constant step of �0.14 GHz. The presented
photonics-based microwave generation method could provide
alternative solution for 5G signal sources in 100-GHz region
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