We focus on the target detection problem of the multiple-input multiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of appli...
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We focus on the target detection problem of the multiple-input multiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deepneuralnetworks (DNNs) to improve the detection performance in a data-driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL-based MIMO radar target detection framework, a deepconvolutionalneuralnetwork (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.
In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex...
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In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex hyperspectral medical images is proposed. The performance of computer-aided diagnosis by verifying the region in hyperspectral image by pre and post-cancerous region classification is enhanced. For this a deep Boltzmann machine (DBM) architecture of the bipartite structure as an unsupervised generative model was developed. The performance of DBM is compared with deep convolutional neural network architecture. For implementation, a three-layer unsupervised network with a backpropagation structure is used. From the presented dataset, image patches are collected and classified into two classes, namely non-informative and discriminative classes as labelled classes. The spatial information is used for classification and spectral-spatial representation of class labels is formed. In the labelled classes, the accuracy, false-positive predictions, sensitivity are obtained for the proposed fully-connected network. By the proposed cognitive computation technique an accuracy of 95.5% with 93.5% sensitivity was obtained. From the obtained classification, accuracy and success rate DBM provide a better classification of complex images compared to traditional convolution network.
An infectious illness known as Pneumonia is often caused by infection due to a bacterium in the alveoli of lungs. When an infected tissue of the lungs has inflammation, it builds up pus in it. To find out if the patie...
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ISBN:
(数字)9781728153131
ISBN:
(纸本)9781728153131
An infectious illness known as Pneumonia is often caused by infection due to a bacterium in the alveoli of lungs. When an infected tissue of the lungs has inflammation, it builds up pus in it. To find out if the patient has Pneumonia, experts conduct physical exams and diagnose their patients through Chest X-ray, ultrasound, or biopsy of lungs. Misdiagnosis, inaccurate treatment, and if the disease is ignored, it may lead to the death of a patient. The progression of deep Learning contributes to the aid in the decision-making process of the experts to diagnose patients with pneumonia. The study employs flexible and efficient approaches of deep learning applying six models of CNN in predicting and recognizing a patient unaffected and affected with the disease employing a chest Xray image. GoogLeNet, LeNet, VGG-16, AlexNet, StridedNet, and ResNet-50 models with a dataset of 28,000 images and using a 224x224 resolution with 32 and 64 batch sizes are applied to verify the performance of each models being trained. The study likewise implements Adam as an optimizer that maintains an adjusted 1e-4 learning rate and an epoch of 500 employed to all the models. Both GoogLeNet and LeNet obtained a 98% rate, VGGNet-16 earned an accuracy rate of 97%, AlexNet and StridedNet model obtained a 96% while the ResNet-50 model obtained 80% during the training of models. GoogleNet and LeNet models achieved the highest accuracy rate for performance training. The six models identified were capable to detect and predict a pneumonia disease including a healthy chest X-ray.
A common and severe respiratory illness that affects people of all ages is pneumonia. To prevent complications and improve clinical outcomes, pneumonia must be identified and treated as soon as possible. Developing an...
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