Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

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Deep Convolutional Neural Networks for Hyperspectral Image Classification

Journal of Sensors ◽

10.1155/2015/258619 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 419

Author(s):

Wei Hu ◽

Yangyu Huang ◽

Li Wei ◽

Fan Zhang ◽

Hengchao Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Hyperspectral Image ◽

Image Data ◽

Classification Performance ◽

Spectral Signature ◽

Support Vector ◽

Data Sets ◽

Deep Convolutional Neural Networks ◽

Visual Tasks

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

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Can Pre-Trained Convolutional Neural Networks be directly used as a Feature Extractor for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v3 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective: In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results: From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

BMC Research Notes ◽

10.1186/s13104-020-05343-4 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen Chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Future Work

Abstract Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026817500018 ◽

2017 ◽

Vol 16 (01) ◽

pp. 1750001 ◽

Cited By ~ 23

Author(s):

Long Yu ◽

Zhiyin Wang ◽

Shengwei Tian ◽

Feiyue Ye ◽

Jianli Ding ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Water Body ◽

Spatial Information ◽

Landsat Imagery ◽

Classification Performance ◽

Support Vector ◽

Learning Methods ◽

Wide Range

Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).

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Injection of Traditional Hand-Crafted Features into Modern CNN-based Models for SAR Ship Classification: What, Why, Where, and How

Remote Sensing ◽

10.3390/rs13112091 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2091

Author(s):

Tianwen Zhang ◽

Xiaoling Zhang

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Classification Performance ◽

Synthetic Aperture ◽

Injection Technique ◽

Accuracy Improvement ◽

Deep Networks ◽

Ship Classification

With the rise of artificial intelligence, many advanced synthetic aperture radar (SAR) ship classifiers based on convolutional neural networks (CNNs) have achieved better accuracies than traditional hand-crafted feature ones. However, most existing CNN-based models uncritically abandon traditional hand-crafted features, and rely excessively on abstract ones of deep networks. This may be controversial, potentially creating challenges to improve classification performance further. Therefore, in view of this situation, this paper explores preliminarily the possibility of injection of traditional hand-crafted features into modern CNN-based models to further improve SAR ship classification accuracy. Specifically, we will — (1) illustrate what this injection technique is, (2) explain why it is needed, (3) discuss where it should be applied, and (4) describe how it is implemented. Experimental results on the two open three-category OpenSARShip-1.0 and seven-category FUSAR-Ship datasets indicate that it is effective to perform injection of traditional hand-crafted features into CNN-based models to improve classification accuracy. Notably, the maximum accuracy improvement reaches 6.75%. Hence, we hold the view that it is not advisable to abandon uncritically traditional hand-crafted features, because they can also play an important role in CNN-based models.

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Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v1 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNN’s) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNN’s as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNN’s, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

BMC Medical Imaging ◽

10.1186/s12880-021-00680-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Magdalena Dobrolińska ◽

Niels van der Werf ◽

Marcel Greuter ◽

Beibei Jiang ◽

Riemer Slart ◽

...

Keyword(s):

Neural Networks ◽

Radiation Dose ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Classification Performance ◽

Influential Factors ◽

Motion Artifacts ◽

Reconstruction Method ◽

Reconstruction Algorithms ◽

Ct System

Abstract Background Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance. Methods Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models. Results The accuracy of the CNN classification was 90.2 ± 3.1%, 90.6 ± 3.5%, and 90.1 ± 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P < 0.001). The classification accuracy in all three CNN architectures was not affected by CT system, radiation dose or image reconstruction method (all P > 0.05). Conclusions The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores.

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Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v2 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective: In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet.Results: From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs10101649 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1649 ◽

Cited By ~ 15

Author(s):

Hao Li ◽

Pedram Ghamisi ◽

Uwe Soergel ◽

Xiao Zhu

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Rural Areas ◽

Remote Sensing Data ◽

Classification Performance ◽

Sensor Data ◽

Support Vector ◽

Data Sets ◽

Data Set ◽

Multi Sensor Data Fusion

Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. First, extinction profiles are applied to both data sources in order to extract spatial and elevation features from hyperspectral and LiDAR-derived data, respectively. Second, a three-stream CNN is designed to extract informative spectral, spatial, and elevation features individually from both available sources. The combination of extinction profiles and CNN features enables us to jointly benefit from low-level and high-level features to improve classification performance. To fuse the heterogeneous spectral, spatial, and elevation features extracted by CNN, instead of a simple stacking strategy, a multi-sensor composite kernels (MCK) scheme is designed. This scheme helps us to achieve higher spectral, spatial, and elevation separability of the extracted features and effectively perform multi-sensor data fusion in kernel space. In this context, a support vector machine and extreme learning machine with their composite kernels version are employed to produce the final classification result. The proposed framework is carried out on two widely used data sets with different characteristics: an urban data set captured over Houston, USA, and a rural data set captured over Trento, Italy. The proposed framework yields the highest OA of 92 . 57 % and 97 . 91 % for Houston and Trento data sets. Experimental results confirm that the proposed fusion framework can produce competitive results in both urban and rural areas in terms of classification accuracy, and significantly mitigate the salt and pepper noise in classification maps.

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Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Sensors ◽

10.3390/s21030764 ◽

2021 ◽

Vol 21 (3) ◽

pp. 764

Author(s):

Zhiwen Huang ◽

Quan Zhou ◽

Xingxing Zhu ◽

Xuming Zhang

Keyword(s):

Neural Networks ◽

Image Classification ◽

Convolutional Neural Networks ◽

Medical Image ◽

Image Data ◽

Classification Performance ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

Triplet Loss ◽

Medical Image Classification

In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

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