Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images

X Ray ◽

Proposed Model ◽

Sensitivity Specificity ◽

Better Than

In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar.

Incorporating Context-Relevant Knowledge into Convolutional Neural Networks for Short Text Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110067 ◽

2019 ◽

Vol 33 ◽

pp. 10067-10068 ◽

Cited By ~ 2

Author(s):

Jingyun Xu ◽

Yi Cai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Text Classification ◽

Classification Methods ◽

Short Text ◽

Proposed Model ◽

High Level ◽

Context Features

Some text classification methods don’t work well on short texts due to the data sparsity. What’s more, they don’t fully exploit context-relevant knowledge. In order to tackle these problems, we propose a neural network to incorporate context-relevant knowledge into a convolutional neural network for short text classification. Our model consists of two modules. The first module utilizes two layers to extract concept and context features respectively and then employs an attention layer to extract those context-relevant concepts. The second module utilizes a convolutional neural network to extract high-level features from the word and the contextrelevant concept features. The experimental results on three datasets show that our proposed model outperforms the stateof-the-art models.

Classification of tectonic and non-tectonic seismicity based on convolutional neural network

Geophysical Journal International ◽

10.1093/gji/ggaa444 ◽

2020 ◽

Vol 224 (1) ◽

pp. 191-198

Author(s):

Xinliang Liu ◽

Tao Ren ◽

Hongfeng Chen ◽

Yufeng Chen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Proposed Model ◽

Single Station ◽

Using Data ◽

Fully Connected

SUMMARY In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity.

Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning

Applied Engineering in Agriculture ◽

10.13031/aea.13794 ◽

2020 ◽

Vol 36 (5) ◽

pp. 743-749

Author(s):

Xingwang Li ◽

Xiaofei Fan ◽

Lili Zhao ◽

Sheng Huang ◽

Yi He ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Classification Accuracy ◽

Multispectral Imaging ◽

Classification Model ◽

One Dimensional ◽

Pepper Seed ◽

Better Than

HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.

Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by GAN

International Journal of Image and Graphics ◽

10.1142/s021946782350002x ◽

2021 ◽

Author(s):

Ramesh Adhikari ◽

Suresh Pokharel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Data Augmentation ◽

Medical Diagnostics ◽

Generative Adversarial Networks ◽

Generalization Capability ◽

X Ray ◽

Original Dataset ◽

Unseen Data

Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2% when the augmented data were used along with the originally available dataset.

Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i4.pp328-341 ◽

2019 ◽

Vol 8 (4) ◽

pp. 328

Author(s):

Sachin B. Jadhav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Plant Diseases ◽

Experimental Results ◽

Disease Classification ◽

Soybean Leaves ◽

Soybean Diseases ◽

Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

10.21203/rs.3.rs-952656/v1 ◽

2021 ◽

Author(s):

Shima Baniadamdizaj ◽

Mohammadreza Soheili ◽

Azadeh Mansouri

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Convolutional Neural Networks ◽

Opposite Hand ◽

Processing Strategies ◽

Document Localization

Abstract Today integration of facts from virtual and paper files may be very vital for the expertise control of efficient. This calls for the record to be localized at the photograph. Several strategies had been proposed to resolve this trouble; however, they may be primarily based totally on conventional photograph processing strategies that aren't sturdy to intense viewpoints and backgrounds. Deep Convolutional Neural Networks (CNNs), on the opposite hand, have demonstrated to be extraordinarily sturdy to versions in history and viewing attitude for item detection and classification responsibilities. We endorse new utilization of Neural Networks (NNs) for the localization trouble as a localization trouble. The proposed technique ought to even localize photos that don't have a very square shape. Also, we used a newly accrued dataset that has extra tough responsibilities internal and is in the direction of a slipshod user. The end result knowledgeable in 3 exclusive classes of photos and our proposed technique has 83% on average. The end result is as compared with the maximum famous record localization strategies and cell applications.

The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.1.16786 ◽

2018 ◽

Vol 7 (3.1) ◽

pp. 13

Author(s):

Raveendra K ◽

R Vinoth Kanna

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Image Retrieval ◽

Document Image ◽

Retrieval Process ◽

Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

A Survey on Techniques used in Medical Imaging Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/2089/1/012013 ◽

2021 ◽

Vol 2089 (1) ◽

pp. 012013

Author(s):

Priyadarshini Chatterjee ◽

Dutta Sushama Rani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Analysis ◽

Network Architecture ◽

Medical Image Analysis ◽

Artificial Neural ◽

Automated Screening ◽

To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

Using Convolutional Neural Networks for the Detection of Spinal Transverse Processes

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.13355 ◽

2019 ◽

Author(s):

Victoria Wu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Transverse Process ◽

Classification Model ◽

Ultrasound Images ◽

Ultrasound Scan ◽

X Ray ◽

Scoliosis Screening

Introduction: Scoliosis, an excessive curvature of the spine, affects approximately 1 in 1,000 individuals. As a result, there have formerly been implementations of mandatory scoliosis screening procedures. Screening programs are no longer widely used as the harms often outweigh the benefits; it causes many adolescents to undergo frequent diagnosis X-ray procedure This makes spinal ultrasounds an ideal substitute for scoliosis screening in patients, as it does not expose them to those levels of radiation. Spinal curvatures can be accurately computed from the location of spinal transverse processes, by measuring the vertebral angle from a reference line [1]. However, ultrasound images are less clear than x-ray images, making it difficult to identify the spinal processes. To overcome this, we employ deep learning using a convolutional neural network, which is a powerful tool for computer vision and image classification [2]. Method: A total of 2,752 ultrasound images were recorded from a spine phantom to train a convolutional neural network. Subsequently, we took another recording of 747 images to be used for testing. All the ultrasound images from the scans were then segmented manually, using the 3D Slicer (www.slicer.org) software. Next, the dataset was fed through a convolutional neural network. The network used was a modified version of GoogLeNet (Inception v1), with 2 linearly stacked inception models. This network was chosen because it provided a balance between accurate performance, and time efficient computations. Results: Deep learning classification using the Inception model achieved an accuracy of 84% for the phantom scan. Conclusion: The classification model performs with considerable accuracy. Better accuracy needs to be achieved, possibly with more available data and improvements in the classification model. Acknowledgements: G. Fichtinger is supported as a Canada Research Chair in Computer-Integrated Surgery. This work was funded, in part, by NIH/NIBIB and NIH/NIGMS (via grant 1R01EB021396-01A1 - Slicer+PLUS: Point-of-Care Ultrasound) and by CANARIE’s Research Software Program. Figure 1: Ultrasound scan containing a transverse process (left), and ultrasound scan containing no transverse process (right). Figure 2: Accuracy of classification for training (red) and validation (blue). References: Ungi T, King F, Kempston M, Keri Z, Lasso A, Mousavi P, Rudan J, Borschneck DP, Fichtinger G. Spinal Curvature Measurement by Tracked Ultrasound Snapshots. Ultrasound in Medicine and Biology, 40(2):447-54, Feb 2014. Krizhevsky A, Sutskeyer I, Hinton GE. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25:1097-1105.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i2.2754 ◽

2021 ◽

Vol 5 (2) ◽

pp. 312-318

Author(s):

Rima Dias Ramadhani ◽

Afandi Nur Aziz Thohari ◽

Condro Kartiko ◽

Apri Junaidi ◽

Tri Ginanjar Laksana ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Science ◽

No Value ◽

Training Process ◽

Indonesian Government ◽

Speed Up ◽

Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.