Search

Over the past few years, tasks such as automatic information extraction and object surface detection from 3D medical images have become increasingly popular, providing clinicians with numerous new opportunities for exploration and diagnostic assistance. Nonetheless, the segmentation process can present various challenges. This paper introduces an automatic 3D deep parametric active surface model (3D B-snake) approximated by bivariate Unified and Extended spline (UE-spline) functions with a parameter controlling the shape of the snake. We introduce an energy term that enables to separate the different textures present in an image using a Convolutional Neural Network (CNN) learning model, namely U-Net. After balancing our snake surface to align it with the complex parts of the object to be segmented, an object snake alignment strategy is proposed using an interpolation scheme that exploits some geometric properties of the object. Experiments performed on the BraTS21 dataset show that our model is very powerful to deal with various difficulties encountered in medical image segmentation. [ABSTRACT FROM AUTHOR]

• This paper proposes a human–machine integration ECG intelligent annotation system. • The ECG intelligent annotation system can free the annotation experts from the heavy manual annotation work. • This paper proposes an accurate beat generation model suitable for all beat types. • The generated beat data can be used as a supplement to labelled data. • This paper proposes an intelligent beat pre-annotation model based on intelligent simulation generated data. • The intelligent beat pre-annotation model achieved the best performance. An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves. Manual ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development. This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation. Since beat annotation is the most basic and important part, a GAN-based generation model that can generate 14 types of simulation beats and a CNN-based beat pre-annotation model are proposed. The experimental results show that the simulation beat has high similarity to real beat and the accuracy of the pre-annotation model on the test set of 14 classes of beats is 99.28%. The proposed ECG intelligent annotation system's self-learning mechanism could improve pre-annotation performance and annotation efficiency by generating more labelled data. The proposed annotation system can also be extended to other data annotation applications. [ABSTRACT FROM AUTHOR]

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse. [ABSTRACT FROM AUTHOR]

Handwritten signature is one of the most important behavioral biometrics and plays an important role in the field of identity verification. It is regarded as a legal means to verify personal identity by administrative and financial institutions. Traditional manual signature verification requires large labor costs and the probability of verification error is relatively high. Nowadays, tablets are often used for signature capturing, which motivates us to explore the feasibility of using tablets for signature verification. In this paper, we propose an online handwriting s ignature v erification system based on s ound and v ibration (SVSV) generated during the signing process. We develop an application to collect signature-related vibration and sound data. We first extract the time domain features of the sound signal and use Fast Fourier Transform to extract the frequency domain features of the sound data. For vibration data, we use Discrete Cosine Transform for dimensionality reduction and feature extraction. Then we fuse the sound and vibration features. Finally, we design an efficient one-class classifier based on the Convolutional Neural Network to perform signature verification. Through extensive experiments with 12 volunteers, the results show that SVSV is a robust and efficient system with an AUC of 0.984 and an EER of 0.05. [ABSTRACT FROM AUTHOR]

Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts using deep learning techniques to create balanced datasets. The FaultFace methodology uses 2D representations of vibration signals denominated faceportraits obtained by time–frequency transformation techniques. From the obtained faceportraits, a Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset. A Convolutional Neural Network is trained for fault detection employing the balanced dataset. The FaultFace methodology is compared with other deep learning techniques to evaluate its performance in for fault detection with unbalanced datasets. Obtained results show that FaultFace methodology has a good performance for failure detection for unbalanced datasets. [ABSTRACT FROM AUTHOR]

The natural language processing (NLP) denotes a technique used to process data such as text and speech. Some of the fundamental research in NLP includes the named entity recognition, which recognizes the named entities (i.e., persons and companies) from texts, the semantic parsing, which converts a natural language utterance to a logical form, and the co-reference resolution, which extracts the nouns (including pronouns and noun phrases) pointing to the same reference body. In this paper, we focus on the mention extraction and classification, proposing a neural-encoded mention-hypergraph model named the BILU-NEMH to extract the mention entities from a content. The proposed BILU-NEMH model combines a mention hypergraph model with the encoding schema and neural network. The proposed model can effectively capture the overlapping mention entities of an unbounded length. The proposed model was verified by the experiments, and the obtained experimental results showed that the proposed model achieved better performance and greater effectiveness than the existing related models on most standard datasets. [ABSTRACT FROM AUTHOR]

Abstract Fibrillations and flutters are serious diseases influence the normal functioning of the heart. Among the most frequently occurring heart disorders belong atrial fibrillation (A fib), atrial flutter (A fl), and ventricular fibrillation (V fib). Nowadays, heart failures are mostly detected by electrocardiogram (ECG) device by examining the signal transferred from electrodes placed on the human body to the output display. The signal is examined by professional health personnel, who are looking for an obvious pattern representing the normal or abnormal rhythm of the heart. Nevertheless, information from ECG can be distorted by noise on data transmission. Moreover,problematic pattern does not have to be so much different from normal and it can be difficult to recognize them just by human eye even by an expert in the field. An automated computer-aided diagnosis (CAD) is an approach to make decision support for elimination of these lacks. For early diagnosis, CAD tool should work in like real-time system without big time consuming and dependency on data and measuring differences of each device. This paper proposes a novel approach of a CAD system to the detection of fibrillations and flutters by our 8-layer deep convolutional neural network. Proposed model requires only basic data normalization without pre-processing and feature extraction from raw ECG samples. We have achieved the accuracy, specificity, and sensitivity of 98.45%, 99.27%, and 99.87% respectively. Designed system can be directly implemented like decision support system in clinical environment. [ABSTRACT FROM AUTHOR]

• A shock detection method based on Convolutional Neural Networks is proposed. • A novel loss function is designed for shock waves. • Compared with existing methods, ours is more efficient or yields better results. • The good generalization of our method is demonstrated by extensive experiments. As one of the main technologies of flow visualization, shock detection plays a key role in feature identification and has been intensively studied. However, existing methods take too much execution time to meet the requirement of post-processing on large-scale Computational Fluid Dynamics (CFD) flow field data. To address this problem, in this paper, we propose a detection method for shock waves based on Convolutional Neural Networks (CNN) and design a novel loss function to optimize the detection results. In specific, the proposed method samples small patches from flow field data, and trains a detection network which includes multiple convolutional layers. This network is responsible for generating shock values and finding the location of shock waves. Compared with the existing shock detection methods which are not based on deep learning, our method has great advantages in detection time. Compared with the ones based on deep learning, our method gives a better detection result of shock waves. Extensive experimental results demonstrate the good generalization of the proposed method on many datasets. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the hyperchaotic system of 6th-order cellular neural network (CNN), realizes its synchronization based on state observer. In addition, a multi-ary number communication system based on this hyperchaotic system is given in this paper. This communication system has the features of large capacity of signals transmission and high security. [Copyright &y& Elsevier]

Glaucoma progressively affects the optic nerve and may cause partial or complete vision loss. Raised intravascular pressure is the only factor which can be modified to prevent blindness from this condition. Accurate early detection and continuous screening may prevent the vision loss. Computer aided diagnosis (CAD) is a non-invasive technique which can detect the glaucoma in its early stage using digital fundus images. Developing such a system require diverse huge database in order to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of glaucoma using deep learning technique. An eighteen layer convolutional neural networks (CNN) is effectively trained in order to extract robust features from the digital fundus images. Finally these features are classified into normal and glaucoma classes during testing. We have achieved the highest accuracy of 98.13% using 1426 (589: normal and 837: glaucoma) fundus images. Our experimental results demonstrates the robustness of the system, which can be used as a supplementary tool for the clinicians to validate their decisions. [ABSTRACT FROM AUTHOR]

This paper presents an effective deep attention network for joint hand gesture localization and recognition using static RGB-D images. Our method trains a CNN framework based on a soft attention mechanism in an end-to-end manner, which is capable of automatically localizing hands and classifying gestures using a single network rather than relying on the conventional means of stage-wise hand segmentation/detection and classification. More precisely, our attention network first computes the weight for each proposal generated from the entire image, in order to judge the probability of the hand appearing in a given region. It then implements a global-sum operation for all proposals, which is influenced by their corresponding weights, in order to obtain a representation of the entire image. We demonstrate the feasibility and effectiveness of our method through extensive experiments on the NTU Hand Digits (NTU-HD) benchmark and the challenging HUST American Sign Language (HUST-ASL) dataset. Moreover, the proposed attention network is simple to train, without requiring bounding-box or segmentation mask annotations, which makes it easy to apply in hand gesture recognition systems. Based on the proposed attention network and taken RGB-D images as input, we obtain the state-of-the-art hand gesture recognition performance on the challenging HUST-ASL dataset. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) could be damaged by low-velocity impacts. Precise localization of impact can increase the efficiency of nondestructive testing by narrowing the scanning in a small area. The conventional localization method is based on the time-of-arrival (ToA) of impact induced guided wave, whose localization accuracy could be influenced by the reflected waves from plate edges. This paper proposes an impact localization method based on time–frequency features of guided wave and convolutional neural network. To test the algorithm, 585 impact experiments are performed at 117 different locations of a CFRP plate. Signals corresponding to 12 randomly selected locations are treated as test set, and the remaining signals are used for training of the network. The results show that the average localization error of the proposed method is 10.2 mm. • The influence of reflected ultrasonic guided wave on the localization of impact is analyzed. • The time–frequency features of guided wave signal is analyzed with wavelet transform. • A convolutional neural network is trained to locate the impacts on a CFRP plate. [ABSTRACT FROM AUTHOR]

The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories – Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19. [ABSTRACT FROM AUTHOR]

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in ≤ 2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. [ABSTRACT FROM AUTHOR]