Inception-ResNet-V2 The U-Net Encoder for Road Segmentation using Sentinel 2A

The deep learning convolution neural network (DL-CNN) benefits evaluating clot burden of acute pulmonary thromboembolism (APE). Our objective was to compare the performance of the deep learning convolution neural network trained by the fine-tuning [DL-CNN (ft)] and the deep learning convolution neural network trained from the scratch [DL-CNN (fs)] in the quantitative assessment of APE. We included the data of 680 cases for training DL-CNN by DL-CNN (ft) and DL-CNN (fs), then retrospectively included 410 patients (137 patients with APE, 203 males, mean age 60.3±11.4 years) for testing the models. The distribution and volume of clots were respectively assessed by DL-CNN(ft) and DL-CNN(fs), and sensitivity, specificity, and area under the curve (AUC) were used to evaluate their performances in detecting clots on a per-patient and clot level. Radiologists evaluated the distribution of clots, Qanadli score, and Mastora score and right ventricular metrics, and the correlation of clot volumes with right ventricular metrics were analyzed with Spearman correlation analysis. On a per-patient level, the two DL-CNN models had high sensitivities and moderate specificities [DL-CNN (ft): 100% and 77.29%; DL-CNN (fs): 100% and 75.82%], and their AUCs were comparable (Z=0.30, P=0.38). On a clot level, DL-CNN (ft) and DL-CNN (fs) sensitivities and specificities in detecting central clots were 99.06% and 72.61%, and 100% and 70.63%, respectively. DL-CNN (ft) sensitivities and specificities in detecting peripheral clots were mostly higher than those of DL-CNN (fs), and their AUCs were comparable. Clot volumes measured with the two models were similar (U=85094.500, P=0.741), and significantly correlated with Qanadli scores [DL-CNN(ft) r=0.825, P<0.001, DL-CNN(fs) r=0.827, P<0.001] and Mastora scores [DL-CNN(ft) r=0.859, P<0.001, DL-CNN(fs) r=0.864, P<0.001]. Clot volumes were also correlated with right ventricular metrics. Clot burdens were increased in the low-risk, moderate-risk, and high-risk patients. Binary logistic regression revealed that only the ratio of right ventricular area/left ventricular area (RVa/LVa) was an independent predictor of in-hospital death (odds ratio 6.73; 95% CI, 2.7-18.12, P<0.001). Both DL-CNN (ft) and DL-CNN (fs) have high sensitivities and moderate specificities in detecting clots associated with APE, and their performances are comparable. While clot burdens quantitatively calculated by the two DL-CNN models are correlated with right ventricular function and risk stratification, RVa/LVa is an independent prognostic factor of in-hospital death in patients with APE.

For large and medium-sized cities, the planning and development of urban road networks may not keep pace with the growth of urban vehicles, resulting in traffic congestion on urban roads during peak hours. Take Jinan, a mid-sized city in China’s Shandong Province, for example. In view of the daily traffic jam of the city’s road traffic, through investigation and analysis, the existing problems of the road traffic are found out. Based on real-time, daily road traffic data, combined with the existing road network and the planned road network, the application of a road intelligent transportation system is proposed. Combined with the application of a road intelligent transportation system, this paper discusses the future development of urban road traffic and puts forward improvement suggestions for road traffic planning. This paper has reference value for city development, road network construction, the application of intelligent transportation systems, and road traffic planning.

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

Recently, transforming windows files into images and its analysis using machine learning and deep learning have been considered as a state‐of‐the art works for malware detection and classification. This is mainly due to the fact that image‐based malware detection and classification is platform independent, and the recent surge of success of deep learning model performance in image classification. Literature survey shows that convolutional neural network (CNN) deep learning methods are successfully employed for image‐based windows malware classification. However, the malwares were embedded in a tiny portion in the overall image representation. Identifying and locating these affected tiny portions is important to achieve a good malware classification accuracy. In this work, a multi‐headed attention based approach is integrated to a CNN to locate and identify the tiny infected regions in the overall image. A detailed investigation and analysis of the proposed method was done on a malware image dataset. The performance of the proposed multi‐headed attention‐based CNN approach was compared with various non‐attention‐CNN‐based approaches on various data splits of training and testing malware image benchmark dataset. In all the data‐splits, the attention‐based CNN method outperformed non‐attention‐based CNN methods while ensuring computational efficiency. Most importantly, most of the methods show consistent performance on all the data splits of training and testing and that illuminates multi‐headed attention with CNN model's generalizability to perform on the diverse datasets. With less number of trainable parameters, the proposed method has achieved an accuracy of 99% to classify the 25 malware families and performed better than the existing non‐attention based methods. The proposed method can be applied on any operating system and it has the capability to detect packed malware, metamorphic malware, obfuscated malware, malware family variants, and polymorphic malware. In addition, the proposed method is malware file agnostic and avoids usual methods such as disassembly, de‐compiling, de‐obfuscation, or execution of the malware binary in a virtual environment in detecting malware and classifying malware into their malware family.

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.

Autonomous damage identification of submerged structure-foundation systems is challenging due to the difficulty of acquiring damage-induced system responses for training deep learning models. In this study, a novel approach integrating pseudo-damage simulation and convolutional neural network (CNN) deep learning is proposed for damage identification in the submerged structure-foundation system. Pseudo-damage simulation is a technique to generate equivalent damage conditions in inaccessible submerged sub-systems for training deep learning models. The following approaches are implemented to achieve the objective. Firstly, a scheme of pseudo-damage simulation for 1-D CNN deep learning is designed for the caisson-foundation system. Secondly, a vibration monitoring method using pseudo-wave-impulse excitations is designed for the caisson-foundation system. Thirdly, 1-D CNN models are trained for individual caisson units to predict the location and size of foundation damage by vibration signals out of a series of pseudo-damage cases. The 1-D CNN models demonstrate accurate performance in handling untrained scenarios. Experimental results validate the effectiveness of the proposed approach in achieving high accuracy for identification of the foundation damage.

Map-matching algorithms that utilise road segment connectivity along with other data (i.e. position, speed and heading) in the process of map-matching are normally suitable for high frequency (1Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A∗ search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory.The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1s, 5s, 30s, 60s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the optimal input data time lags and amounts of spatial data are determined by a spatio-temporal feature selection algorithm (STFSA), and selected spatio-temporal traffic flow features are extracted from actual data and converted into a two-dimensional matrix. The CNN then learns these features to construct a predictive model. The effectiveness of the proposed method is evaluated by comparing the forecast results with actual traffic data. Other existing models are also evaluated for comparison. The proposed method outperforms baseline models in terms of accuracy.

Facial expression recognition technology has been more and more in demand in security, entertainment, education, medical, and other domains as artificial intelligence has advanced, and face expression recognition technology based on deep learning has become one of the research hotspots. However, there are still some issues with the existing deep learning convolutional neural network; the feature extraction technique has to be improved, and the design of the detailed network structure needs to be optimized. It is critical to do more research on the deep learning convolutional neural network model in order to increase the accuracy of face facial expression detection. In this paper, a deep learning convolutional neural network structure combining VGG16 convolutional neural network and long and short-term memory networks is designed to address the shortcomings of existing deep learning methods in face expression recognition, which are prone to overfitting and gradient disappearance, resulting in low test accuracy. This structure easily and effectively collects facial expression information and then classifies the retrieved features using a support vector machine to detect face expressions. Finally, the fer2013 dataset is used to train face expression recognition, and the results demonstrate that the built deep convolutional neural network model can effectively increase face expression identification accuracy.

Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P=0.092), 71.0% (95% CI [62.6-78.1]; P=0.256) and 24 (95% CI [11.6-48.4]; P=0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR=12) but best with CNN verification (DOR=98). The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.

The global consumption trend of facial skin care products market is gradually changing. With the concept of preventing aging from becoming more common, the age level of using facial skin care products is gradually reduced, so that the demand of young consumer groups gradually increases. This paper used a deep learning algorithm based on the combination of a smart phone and facial skin detection to develop a facial skin image classification system using Convolutional Neural Networks (CNN) deep learning algorithm. In this system, it can recognize three classes facial skin problem, good facial skin quality, bad facial skin quality and face makeup, which helps people quickly understand their facial skin problem. We proposed two different CNN architectures. One has two convolutional layers, two pooling layers and three fully connected layer and the other has three convolution layers, three pooling layers, and four fully connected layer. Finally, we compare the result of our proposed architecture with LeNet-5. From the experimental result, we understand that the architecture which has three convolution layers, three pooling layers, and four fully connected layer, has the highest recognition rate, and we use it as a baseline to build a framework for detecting facial skin problems.

cOVID-19 is a global pandemic that occurred in March 2020. COVID-19 spreads very quickly because it is an infectious disease. COVID-19 has similar characteristics to Pneumonia. The X-Ray results of COVID-19 and Pneumonia can also be said to be similar, making it difficult to distinguish. The object of detection is beneficial to the medical community, especially radiologists, who utilize it to diagnose patients with COVID-19. COVID-19 can be found by using X-Ray images in the medical field. In detecting COVID-19, there are usually many methods that can be used, one of which is deep learning. Convolutional Neural Network (CNN) is a Deep Learning model that can be used to detect images. This research examines previous research on the detection of COVID-19 using CNN's Deep Learning Method, many existing models for COVID-19 detection studies, and some researchers-built models using CNN's Deep Learning Method. The study shows that CNN's Deep Learning accurately detects COVID-19, Negative COVID-19, and Pneumonia. The Multi-layered CNN model uses 3.990 X-Ray images and offers good accuracy, sensitivity, and specificity

As compared with benign, the common human malignancy of skin cancer can be diagnosed visually starting from clinical screening and ending with histopathological examination. Accurate automatic classification of skin lesion images is a great challenge as the image features are very close in these images. In this paper we used two methods for image recognition. The first method was carried out with Convolution neural networks (CNN) that promise to provide a potential classifier for skin lesions. This work presents a dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. It presents a classifier with a single CNN to automatically recognize benign and malignant skin images. As for the second method, we used the Support Vector Machine. Which is a supervised learning algorithm and it is used for classification of data for the different classes based on a separating hyperplane. The network inputs are only disease labels and image pixels. About 320 clinical images of the different diseases have been used to train the CNN. The model performance has been tested with untrained images from the two labels. This model identifies the most common skin cancers and can be updated with a new unlimited number of images. The DLCNN was trained by the ResNet-50 model and it showed good classification of the benign and malignant skin categories. The ResNet-50 as a DLCNN has achieved a significant recognition rate of more than 97% on the testing images, which proves that the benign and malignant lesion skin images are properly classified. Support vector machine (SVM) classifier for the classification of skin cancer, for the feature extraction step achieved 86.9% accuracy. This means in CNN; we had more accuracy with 11%. These results were attained using MATLAB.