Impact of Convolutional Neural Network Input Parameters on Classification Performance

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules

European Journal of Radiology ◽

10.1016/j.ejrad.2021.109810 ◽

2021 ◽

pp. 109810

Author(s):

Teng Zhang ◽

Yida Wang ◽

Yingli Sun ◽

Mei Yuan ◽

Yan Zhong ◽

...

Keyword(s):

Neural Network ◽

Image Analysis ◽

High Resolution ◽

Convolutional Neural Network ◽

Classification Performance ◽

Ct Image ◽

High Resolution Ct ◽

Ct Image Analysis

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Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2019.1.3420 ◽

2019 ◽

Vol 14 (1) ◽

pp. 124-134 ◽

Cited By ~ 2

Author(s):

Shuai Zhang ◽

Yong Chen ◽

Xiaoling Huang ◽

Yishuai Cai

Keyword(s):

Neural Network ◽

Differential Evolution ◽

Convolutional Neural Network ◽

Text Classification ◽

Differential Evolution Algorithm ◽

Classification Performance ◽

Classification Model ◽

Evolution Algorithm ◽

Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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Label Rectification Learning through Kernel Extreme Learning Machine

Wireless Communications and Mobile Computing ◽

10.1155/2021/6669081 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Qiang Cai ◽

Fenghai Li ◽

Yifan Chen ◽

Haisheng Li ◽

Jian Cao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Extreme Learning Machine ◽

Classification Performance ◽

Considerable Progress ◽

Strong Representation ◽

Kernel Extreme Learning Machine ◽

Classification Tasks ◽

Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

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Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

British Journal of Radiology ◽

10.1259/bjr.20201263 ◽

2021 ◽

pp. 20201263

Author(s):

Mohammad Salehi ◽

Reza Mohammadi ◽

Hamed Ghaffari ◽

Nahid Sadighi ◽

Reza Reiazi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Area Under Curve ◽

Binary Classification ◽

Classification Performance ◽

Learning Approach ◽

Insufficient Data ◽

X Ray ◽

Chest X Ray

Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

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A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽

10.3390/s19122814 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2814 ◽

Cited By ~ 2

Author(s):

Xiaoguang Liu ◽

Huanliang Li ◽

Cunguang Lou ◽

Tie Liang ◽

Xiuling Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Fall Detection ◽

Classification Performance ◽

Daily Activities ◽

Support Vector ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

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Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11141678 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1678 ◽

Cited By ~ 3

Author(s):

Yongyong Fu ◽

Ziran Ye ◽

Jinsong Deng ◽

Xinyu Zheng ◽

Yibo Huang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Contextual Information ◽

Feature Space ◽

Classification Performance ◽

Support Vector ◽

Marine Resources ◽

Marine Aquaculture ◽

Multi Scale ◽

Coastal Marine

Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.

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A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images

Remote Sensing ◽

10.3390/rs12061015 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1015 ◽

Cited By ~ 3

Author(s):

Kan Zeng ◽

Yixiao Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Oil Spill ◽

Data Augmentation ◽

Classification Performance ◽

Deep Convolutional Neural Network ◽

Convolutional Network ◽

Data Set ◽

Oil Spill Detection ◽

Processing Framework

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.

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An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Sensors ◽

10.3390/s20071999 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1999 ◽

Cited By ~ 6

Author(s):

Donghang Yu ◽

Qing Xu ◽

Haitao Guo ◽

Chuan Zhao ◽

Yuzhun Lin ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Visual Recognition ◽

Feature Fusion ◽

Remote Sensing Image ◽

Classification Performance ◽

Image Features ◽

Training Dataset ◽

Scene Classification

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

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Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽

10.3390/e21121199 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1199 ◽

Cited By ~ 5

Author(s):

Hyeon Kyu Lee ◽

Young-Seok Choi

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Continuous Wavelet Transform ◽

Brain Computer Interface ◽

Classification Performance ◽

Computer Interface ◽

Continuous Wavelet ◽

Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

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