An Enhanced Recurrent Convolutional Neural Network for Predicting the Status Stage of Patients with Chronic obstructive Pulmonary Diseases: Method Design (Preprint)

BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in China and has caused serious affect to health and life quality. However, the status stage of a patient is difficult to be accurately assessed because of dynamic changes in the condition and complex risk factors. A rapid and accurate methods to predict disease stage of COPD patients is of great significance. OBJECTIVE This study aims to explore an enhanced recurrent convolutional neural networks model for predicting correct staging of patients with COPD in China for assistant disease prevention and treatment. METHODS Data was collected from The First Affiliate Hospital of Guangzhou Medical University, which had standardized disease registration and follow-up management for 5108 patients with COPD. Our enhanced recurrent convolutional neural network consists of a bidirectional LSTM layer, a convolutional layer, a max-pooling layer, and an output layer. RESULTS The model proposed was evaluated on the real-world clinical dataset of 5108 COPD patients to predict the state stage of the disease. The performance of the proposed model achieved 93.2% in terms of accuracy, outperforming a list of baseline models. CONCLUSIONS This paper proposes an enhanced recurrent convolutional neural network model which is experimented on a real-world clinical dataset containing around 5,000 patients with COPD. The proposed model achieves the best performance on all evaluation metrics indicating its feasibility in predicting the state stage of diseases.

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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences ◽

10.3390/app112110301 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10301

Author(s):

Muhammad Shoaib Farooq ◽

Attique Ur Rehman ◽

Muhammad Idrees ◽

Muhammad Ahsan Raza ◽

Jehad Ali ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real World ◽

Early Stage ◽

Binary Classification ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

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Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

Sensors ◽

10.3390/s20123539 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3539 ◽

Cited By ~ 6

Author(s):

Chang-Cheng Lo ◽

Ching-Hung Lee ◽

Wen-Cheng Huang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Loss Function ◽

Feature Space ◽

Vibration Signals ◽

One Dimensional ◽

Experimental Platform ◽

Proposed Model ◽

The Status ◽

Gear Mechanism

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

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Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽

10.3390/en13071772 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1772 ◽

Cited By ~ 2

Author(s):

Kumar Shivam ◽

Jong-Chyuan Tzou ◽

Shang-Chen Wu

Keyword(s):

Neural Network ◽

Wind Speed ◽

Convolutional Neural Network ◽

Real World ◽

Prediction Models ◽

Short Term Memory ◽

Short Term ◽

Wind Speed Prediction ◽

Proposed Model ◽

Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

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Deep LSTM for Emoji Twitter Sentiment Exploration via Distributed Embeddings

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1521.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1714-1718 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Social Media ◽

Public Relations ◽

Real World ◽

State Of The Art ◽

The State ◽

Digital Marketing ◽

Emotional Status ◽

The Real ◽

Proposed Model

Social media’s sentimental data is the most vital digital marketing platform that can help us to reveal the real world events including qualitative insights to understand people’s visibility about brands, politics, emotional status, and so on. With today’s interrelated world, a public relations disaster can be initiated with one post or a tweet. Conventional sentimental analysis is the process of defining whether the shared post on social media is neutral, positive or negative and has been focused by the Dealers, Administrations to understand public feelings of their products and corporation. However, extensive usage of emoji in social media has attracted an increasing interest. In this proposed framework, we suggest a novel scheme for Twitter sentiment method on emojis by considering pre-trained word and emoji embeddings. We first train our model to learn word, emoji embeddings under positive and negative tweets; later a classifier passes them through a neural network combining LSTM to achieve better performance. Our tests show that the proposed model operational for extracting sentiment-aware emojis and outperforms the state-of-the-art simulations.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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

Sensors ◽

10.3390/s21082648 ◽

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.

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A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20210383 ◽

2021 ◽

Author(s):

Young Hyun Kim ◽

Eun-Gyu Ha ◽

Kug Jin Jeon ◽

Chena Lee ◽

Sang-Sun Han

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Speed ◽

Large Scale ◽

Oral Surgery ◽

Human Identification ◽

Running Time ◽

Automated Method ◽

Image Characteristics ◽

Proposed Model

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) dataset. Methods: In total, 2,760 DPRs from 746 subjects who had 2 to 17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test dataset included the latest DPR of each subject (746 images) and the other DPRs (2,014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, –3, and −5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)–applied images. Results: This model had rank-1,–3, and −5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 sec per epoch, and the prediction time for 746 test DPRs was short (3.2 sec/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

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Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽

10.3390/electronics10222868 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2868

Author(s):

Wenxuan Zhao ◽

Yaqin Zhao ◽

Liqi Feng ◽

Jiaxi Tang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real World ◽

Large Scale ◽

Learning Strategy ◽

Contextual Information ◽

Small Scale ◽

Image Dehazing ◽

Atmospheric Scattering ◽

Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

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Long-term Effects of Telemonitoring on Healthcare Usage in Patients with Heart Failure or COPD (Preprint)

10.2196/preprints.10981 ◽

2018 ◽

Author(s):

Jorien Maria Margaretha van der Burg ◽

Nasir Ahmad Aziz ◽

Maurits C. Kaptein ◽

Martine J.M. Breteler ◽

Joris H. Jansen ◽

...

Keyword(s):

Heart Failure ◽

The Netherlands ◽

Healthcare Costs ◽

Hospital Admissions ◽

Disease Stage ◽

Chronic Obstructive ◽

Home Telemonitoring ◽

Copd Patients ◽

Patients With Heart Failure

UNSTRUCTURED Objective The aim of this study was to evaluate the effectiveness of home telemonitoring in reducing healthcare usage and costs in patients with heart failure or chronic obstructive pulmonary disease (COPD). Design The study was a retrospective observational study with a pre-post research design and a follow-up duration of up to 3 years, based on hospital data collected in the period 2012-2016. Setting Data was collected at the Slingeland Hospital in Doetinchem, The Netherlands. Participants In 2012 the Slingeland Hospital in The Netherlands started a telemonitoring program for patients with COPD or heart failure as part of their usual care. Patients were eligible for the telemonitoring program if they were in an advanced disease stage (New York Heart Association (NYHA) functional class 3 or 4; COPD gold stage 3 or 4), received treatment for their condition by a cardiologist or pulmonary specialist at the Slingeland Hospital, were proficient in Dutch and capable of providing informed consent. Exclusion criteria were absence of the cognitive, physical or logistical ability required to fully participate in the program. Hundred seventy-seven patients with heart failure and 83 patients with COPD enrolled the program between 2012 and 2016. Intervention Using a touchscreen, participants with heart failure recorded their weight (daily), blood pressure and heart rate (once a week) through connected instruments, and completed a questionnaire about their symptoms (once a week). Symptoms in patients with COPD were monitored via the Clinical COPD Questionnaire (CCQ), which participants were asked to complete twice per week. All home registrations were sent via a telemonitoring application (cVitals, FocusCura, Driebergen-Rijssenburg) on the iPad to a medical service center were a trained nurse monitored the data and contacted the patient by video chat or a specialised nurse in the hospital in case of abnormal results, such as deviations from a preset threshold or alterations in symptom score. Outcome measures The primary outcome was the number of hospitalisations; the secondary outcomes were total number of hospitalisation days and healthcare costs during the follow-up period. Generalised Estimating Equations were applied to account for repeated measurements, adjusting for sex, age and length of follow-up. Results In heart failure patients (N=177), after initiation of home telemonitoring both the number of hospitalisations and the total number of hospitalisation days significantly decreased (incidence rate ratio of 0.35 (95% CI: 0.26-0.48) and 0.35 (95% CI: 0.24-0.51), respectively), as did the total healthcare costs (exp(B) = 0.11 (95% CI: 0.08-0.17)), all p < 0.001. In COPD patients (N=83) neither the number of hospitalisations nor the number of hospitalisation days changed compared to the pre-intervention period. However, the average healthcare costs were about 54% lower in COPD patients after the start of the home telemonitoring intervention (exp(B) = 0.46, 95% CI 0.25-0.84, p = 0.011). Conclusion Integrated telemonitoring significantly reduced the number of hospital admissions and days spent in hospital in patients with heart failure, but not in patients with COPD. Importantly, in both patients with heart failure and COPD the intervention substantially reduced the total healthcare costs.

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Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

Current Bioinformatics ◽

10.2174/1574893616666210212084839 ◽

2021 ◽

Vol 16 ◽

Author(s):

Di Gai ◽

Xuanjing Shen ◽

Haipeng Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Melting Curve ◽

Classification Model ◽

Proposed Model ◽

Unbalanced Dataset ◽

Manual Selection ◽

Low Efficiency ◽

Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

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