scholarly journals Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanli Wei ◽  
Ying Zhu ◽  
Xin Wen ◽  
Qing Rui ◽  
Wei Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Deep Convolutional Neural Network ◽  
Advantages And Disadvantages ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Signal ◽  
And Performance

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things

2021 ◽  
Vol 11 (1) ◽  
pp. 283-287
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
High Volume ◽  
Deep Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Batch Size ◽  
Feed Forward

In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.

Automatic classification of pavement crack using deep convolutional neural network

2018 ◽  
Vol 21 (4) ◽  
pp. 457-463 ◽  
Author(s):  
Baoxian Li ◽  
Kelvin C. P. Wang ◽  
Allen Zhang ◽  
Enhui Yang ◽  
Guolong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Deep Convolutional Neural Network

scholarly journals Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Wendong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Data ◽  
Classification Algorithm ◽  
Deep Convolutional Neural Network ◽  
Practical Significance ◽  
Diabetic Patients ◽  
Data Set ◽  
Layer Function ◽  
Deep Cnn

In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network (“CNN+” for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the “CNN+” algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the “CNN+” model can get more reliable prediction results.

Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making

2019 ◽  
Vol 29 (10) ◽  
pp. 5458-5468 ◽  
Author(s):  
Alexander Ciritsis ◽  
Cristina Rossi ◽  
Matthias Eberhard ◽  
Magda Marcon ◽  
Anton S. Becker ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Deep Convolutional Neural Network ◽  
Breast Lesions ◽  
Human Decision

scholarly journals An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 602
Author(s):  
Muhammad Aamir ◽  
Muhammad Irfan ◽  
Tariq Ali ◽  
Ghulam Ali ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Eye Disease ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Multi Level ◽  
Retinal Fundus Images ◽  
And Performance ◽  
Retinal Fundus

Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases.

scholarly journals An Enhanced Musical Instrument Classification using Deep Convolutional Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 8772-8774
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Musical Instrument ◽  
Classification Algorithm ◽  
Deep Convolutional Neural Network ◽  
Experimental Result ◽  
Efficient Tool ◽  
Challenging Issue ◽  
Digital World ◽  
Musical Instrument Classification

Retrieval of musical information from musical databases is a major challenging issue in a digital world. Therefore, it is necessary to develop an efficient tool for retrieving the musical information. Musical instrument classification plays a major role for retrieving the information from musical database. In order to retrieve the musical instrument efficiently, an enhanced musical instrument classification algorithm using deep Convolutional Neural Network is proposed in this paper. The proposed algorithm consists of convolutional layers interleaved with two pooling functions followed by two fully interconnected layers. There are sixteen instruments from different instrument families are taken for evaluating the performance of proposed algorithm. The experimental result shows that the proposed algorithm recognizes the instruments significantly and achieves the greater accuracy than existing algorithm

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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