Detection of Pulmonary Tuberculosis Manifestation in Chest X-Rays using Different Convolutional Neural Network (CNN) Models

Tuberculosis (TB) is airborne infectious disease which has claimed many lives than any other infectious disease. Chest X-rays (CXRs) are often used in recognizing TB manifestation site in chest. Lately, CXRs are taken in digital formats, which has made a huge impact in rapid diagnosis using automated systems in medical field. In our current work, four simple Convolutional Neural Networks (CNN) models such as VGG-16, VGG-19, RestNet50, and GoogLenet are implemented in identification of TB manifested CXRs. Two public TB image datasets were utilized to conduct this research. This study was carried out to explore the limit of accuracies and AUCs acquired by simple and small-scale CNN with complex and large-scale CNN models. The results achieved from this work are compared with results of two previous studies. The results indicate that our proposed VGG-16 model has gained highest score overall compared to the models from other two previous studies.

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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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AI INFLUENCE IN COVID-19 DETECTION

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/07235 ◽

2021 ◽

Vol 23 (07) ◽

pp. 1116-1120

Author(s):

Cijil Benny ◽

Keyword(s):

Neural Network ◽

Statistical Analysis ◽

Convolutional Neural Network ◽

Case Reports ◽

Ct Scans ◽

Data Sets ◽

X Rays ◽

Medical Field ◽

Primary Key ◽

Made In

This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point. The best-analyzing methods on the studies were Susceptible Infected Recovered and Susceptible Exposed Infected Removed respectively. Whereas the implementation of AI is mostly done in X-rays and CT- Scans with the help of a Convolutional Neural Network. To accomplish the paper several data sets are used. They include medical and case reports, medical strategies, and persons respectively. Approaches are being done through shared statistical analysis based on these reports. Considerably the acceptance COVID is being shared and it is also reachable. Furthermore, much regulation is needed for handling this pandemic since it is a threat to global society. And many more discoveries shall be made in the medical field that uses AI as a primary key source.

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Extensive deep neural networks for transferring small scale learning to large scale systems

Chemical Science ◽

10.1039/c8sc04578j ◽

2019 ◽

Vol 10 (15) ◽

pp. 4129-4140 ◽

Cited By ~ 9

Author(s):

Kyle Mills ◽

Kevin Ryczko ◽

Iryna Luchak ◽

Adam Domurad ◽

Chris Beeler ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Large Scale ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Small Scale ◽

Large Scale Systems ◽

Energy Entropy ◽

Large Systems ◽

Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

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Thermodynamic Neural Network

Entropy ◽

10.3390/e22030256 ◽

2020 ◽

Vol 22 (3) ◽

pp. 256

Author(s):

Todd Hylton

Keyword(s):

Neural Network ◽

Large Scale ◽

Open Systems ◽

Self Organization ◽

Small Scale ◽

Edge States ◽

Fluctuation Dissipation ◽

Multiscale Dynamics ◽

Physical Quantities ◽

Open Thermodynamic Systems

A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. Isolated networks show multiscale dynamics, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model integrates concepts of conservation, potentiation, fluctuation, dissipation, adaptation, equilibration and causation to illustrate the thermodynamic evolution of organization in open systems. A key conclusion of the work is that the transport and dissipation of conserved physical quantities drives the self-organization of open thermodynamic systems.

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ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

Processes ◽

10.3390/pr7120893 ◽

2019 ◽

Vol 7 (12) ◽

pp. 893

Author(s):

Xiaoli Wang ◽

He Zhang ◽

Yalin Wang ◽

Shaoming Yang

Keyword(s):

Neural Network ◽

Hybrid Model ◽

Large Scale ◽

Multiscale Model ◽

Small Scale ◽

Activation Functions ◽

Model Accuracy ◽

Process Dynamics ◽

Model Modification ◽

Online Prediction

Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owing to the variation of the feed properties, which changes the process dynamics. The current neural network online prediction models usually use fixed activation functions, and it is not easy to perform dynamic modification. Therefore, a few methods are proposed here. Firstly, an extreme learning machine (ELM)-based single-layer feedforward neural network with activation-function learning (AFL–SLFN) is proposed. The activation functions of the ELM are adjusted to enhance the ELM network structure and accuracy. Then, a hybrid model with adaptive weights is established by using the AFL–SLFN as a sub-model, which improves the prediction accuracy. To track the process dynamics and maintain the generalization ability of the model, a multiscale model-modification strategy is proposed. Here, small-, medium-, and large-scale modification is performed in accordance with the degree and the causes of the decrease in model accuracy. In the small-scale modification, an improved just-in-time local modeling method is used to update the parameters of the hybrid model. In the medium-scale modification, an improved elementary effect (EE)-based Morris pruning method is proposed for optimizing the sub-model structure. Remodeling is adopted in the large-scale modification. Finally, a simulation using industrial process data for tailings grade prediction in a flotation process reveals that the proposed method has better performance than some state-of-the-art methods. The proposed method can achieve rapid online training and allows optimization of the model parameters and structure for improving the model accuracy.

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An empirical test of the role of small-scale transmission in large-scale disease dynamics

10.1101/285080 ◽

2018 ◽

Author(s):

Joseph R. Mihaljevic ◽

Carlos M. Polivka ◽

Constance J. Mehmel ◽

Chentong Li ◽

Vanja Dukic ◽

...

Keyword(s):

Infectious Disease ◽

Disease Transmission ◽

Large Scale ◽

Empirical Test ◽

Disease Model ◽

Individual Variability ◽

Host Susceptibility ◽

Small Scale ◽

Unconstrained Model

AbstractA key assumption of models of infectious disease is that population-scale spread is driven by transmission between host individuals at small scales. This assumption, however, is rarely tested, likely because observing disease transmission between host individuals is non-trivial in many infectious diseases. Quantifying the transmission of insect baculoviruses at a small scale is in contrast straightforward. We fit a disease model to data from baculovirus epizootics (= epidemics in animals) at the scale of whole forests, while using prior parameter distributions constructed from branch-scale experiments. Our experimentally-constrained model fits the large-scale data very well, supporting the role of small-scale transmission mechanisms in baculovirus epizootics. We further compared our experimentally-based model to an unconstrained model that ignores our experimental data, serving as a proxy for models that include large-scale mechanisms. This analysis supports our hypothesis that small-scale mechanisms are important, especially individual variability in host susceptibility to the virus. Comparison of transmission rates in the two models, however, suggests that large-scale mechanisms increase transmission compared to our experimental estimates. Our study shows that small-scale and large-scale mechanisms drive forest-wide epizootics of baculoviruses, and that synthesizing mathematical models with data collected across scales is key to understanding the spread of infectious disease.

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Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Future Internet ◽

10.3390/fi12080137 ◽

2020 ◽

Vol 12 (8) ◽

pp. 137

Author(s):

Bo Jiang ◽

Yanbai He ◽

Rui Chen ◽

Chuanyan Hao ◽

Sijiang Liu ◽

...

Keyword(s):

Neural Network ◽

Data Analysis ◽

Network Model ◽

Neural Network Model ◽

Teaching And Learning ◽

Large Scale ◽

Small Scale ◽

Teaching Improvement ◽

Data Feedback ◽

Learning Data

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

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Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

SLEEP ◽

10.1093/sleep/zsaa161 ◽

2020 ◽

Author(s):

Alexander Neergaard Olesen ◽

Poul Jørgen Jennum ◽

Emmanuel Mignot ◽

Helge Bjarup Dissing Sorensen

Keyword(s):

Neural Network ◽

Large Scale ◽

Deep Neural Network ◽

Sleep Stage ◽

Scoring Systems ◽

Training Data ◽

Future Research ◽

Small Scale ◽

Sleep Stage Scoring ◽

Single Cohort

Abstract Study Objectives Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. Methods A deep neural network model was developed using 15,684 polysomnography studies from five different cohorts. We applied four different scenarios: (1) impact of varying timescales in the model; (2) performance of a single cohort on other cohorts of smaller, greater, or equal size relative to the performance of other cohorts on a single cohort; (3) varying the fraction of mixed-cohort training data compared with using single-origin data; and (4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. Results Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777–0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864–0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787–0.790]; 3: 0.808 ± 0.092, 95% CI [0.807–0.810]; 4: 0.821 ± 0.085, 95% CI [0.819–0.823]). Different cohorts show varying levels of generalization to other cohorts. Conclusions Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.

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Application of RBF-STARMA Model in Shipping Flow Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.893 ◽

2010 ◽

Vol 108-111 ◽

pp. 893-897 ◽

Cited By ~ 1

Author(s):

Hong Qiong Huang ◽

Shu Lan Lin ◽

Tian Hao Tang ◽

Ji Fang Li

Keyword(s):

Neural Network ◽

Hybrid Model ◽

Spatial Data ◽

Large Scale ◽

Linear Trend ◽

Moving Average ◽

Original Data ◽

Spatial And Temporal Variation ◽

Small Scale ◽

Temporal And Spatial

Based on the idea of the neural network, intelligent computing methods are used to analyze temporal and spatial data. We present the temporal and spatial autocorrelation moving average (STARMA) model based on the in-depth systematic study on time sequence of hybrid model. Firstly this paper uses radial basis function neural network to extract the temporal and spatial sequence which is non-stationary caused by large-scale non-linear trend, secondly this paper presents STARMA modeling of small-scale random spatial and temporal variation. Comparative analysis between the original data and the forecasting data shows that proposed hybrid model has better performance of fitting and generalization.

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Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks

Creative Information Technology Journal ◽

10.24076/citec.2021v8i1.261 ◽

2021 ◽

Vol 8 (1) ◽

pp. 9

Author(s):

Buyut Khoirul Umri ◽

Ema Utami ◽

Mei P Kurniawan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Literature Review ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Large Scale ◽

Image Data ◽

X Rays ◽

X Ray ◽

Chest X Ray

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19

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