Microgrids (MGs) are small-scale power systems which interconnect distributed energy resources and loads within clearly defined regions. However, the digital infrastructure used in an MG to relay sensory information a...
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Background:Alzheimer disease (AD) is a severe neurological brain disorder. While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for med...
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Background:Alzheimer disease (AD) is a severe neurological brain disorder. While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for an accurate diagnosis of AD.
Objective:In this paper, a complete computer-aided diagnosis system for the diagnosis of AD has been presented. We investigate the performance of some of the most used ML techniques for AD detection and classification using neuroimages from the Open Access Series of Imaging Studies (OASIS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.
Methods:The system uses artificial neural networks (ANNs) and support vector machines (SVMs) as classifiers, and dimensionality reduction techniques as feature extractors. To retrieve features from the neuroimages, we used principal component analysis (PCA), linear discriminant analysis, and t-distributed stochastic neighbor embedding. These features are fed into feedforward neural networks (FFNNs) and SVM-based ML classifiers. Furthermore, we applied the vision transformer (ViT)-based ANNs in conjunction with data augmentation to distinguish patients with AD from healthy controls.
Results:Experiments were performed on magnetic resonance imaging and positron emission tomography scans. The OASIS dataset included a total of 300 patients, while the ADNI dataset included 231 patients. For OASIS, 90 (30%) patients were healthy and 210 (70%) were severely impaired by AD. Likewise for the ADNI database, a total of 149 (64.5%) patients with AD were detected and 82 (35.5%) patients were used as healthy controls. An important difference was established between healthy patients and patients with AD (P=.02). We examined the effectiveness of the three feature extractors and classifiers using 5-fold cross-validation and confusion matrix-based standard classification metrics, namely, a
Multispectral disparity estimation is a difficult task for many reasons: it has all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in a...
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Machine Learning software documentation is different from most of the documentations that were studied in softwareengineering research. Often, the users of these documentations are not software experts. The increasin...
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Device-to-device (D2D) communications have been pro- posed as one of the key technologies to improve the spectral efficiency in the future fifth generation (5G) of wireless mobile communication systems through resourc...
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Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration asso...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration associated with TPM acquisitions and the complexity of microvascular networks. Here, we propose a fully automated pipeline capable of providing useful anatomical models of vascular structures captured with TPM. In the proposed method, we segment blood vessels using a fully convolutional neural network and employ the resulting binary labels to create an initial geometric graph enclosed within vessels boundaries. The initial geometry is then decimated and refined to form graphed curve skeletons that can retain both the vascular shape and its topology. We validate the proposed method on 3D realistic TPM angiographies and compare our results with that obtained through manual annotations.
Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution...
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Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful informat...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful information by its contours. While deep learning models have reached high performances in the segmentation of anatomical structures, the large number of training parameters is a concern since it increases memory usage and reduces the generalization of the model. To address this, a deep CNN model called Dense-Unet is proposed in which, by dense connectivity between various layers, information flow increases throughout the network. This lets us design a network with significantly fewer parameters while keeping the segmentation robust. To the best of our knowledge, Dense-Unet is the lightest deep model proposed for the segmentation of lung fields in CXR images. The model is evaluated on the JSRT and Montgomery datasets and experiments show that the performance of the proposed model is comparable with state-of-the-art methods.
The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of operations that ar...
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"THOSE ARE THE KINDS of bubbles we want to see," says Rebecca White, pointing to tiny pockets of gas that are barely visible on the surface of an artificial pond in New Mexico. Their small size means the car...
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"THOSE ARE THE KINDS of bubbles we want to see," says Rebecca White, pointing to tiny pockets of gas that are barely visible on the surface of an artificial pond in New Mexico. Their small size means the carbon dioxide she and her colleagues have injected into the water has mostly dissolved, instead of just escaping into the air. We're less than a kilometer from the U.S.-Mexican border, surrounded by desert grasslands. It's one of the last places I'd expect to find a thriving population of marine algae. Yet here in this massive pool swirls more than a million liters of Nannochloropsis, a salt-loving alga that flourishes on the brackish water pumped from below.
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