Deep-Feature-Based Approach to Marine Debris Classification

The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of 91.40% and F1-score 92.08%, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.

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Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

Scientific Reports ◽

10.1038/s41598-021-92776-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Andrew P. Creagh ◽

Florian Lipsmeier ◽

Michael Lindemann ◽

Maarten De Vos

Keyword(s):

Multiple Sclerosis ◽

Deep Learning ◽

Inertial Sensor ◽

Heterogeneous Data ◽

Fine Tuning ◽

Sensor Data ◽

Support Vector ◽

Deep Convolutional Neural Networks ◽

Healthcare Applications ◽

Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

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Space Precession Target Classification Based on Radar High-Resolution Range Profiles

International Journal of Antennas and Propagation ◽

10.1155/2019/8151620 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Yizhe Wang ◽

Cunqian Feng ◽

Yongshun Zhang ◽

Sisan He

Keyword(s):

Parameter Extraction ◽

Classification Performance ◽

Support Vector ◽

Electromagnetic Data ◽

Feature Extractor ◽

Different Types ◽

Radar Echo ◽

High Level ◽

Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

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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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A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

Electronics ◽

10.3390/electronics8070783 ◽

2019 ◽

Vol 8 (7) ◽

pp. 783 ◽

Cited By ~ 8

Author(s):

Edoardo Ragusa ◽

Erik Cambria ◽

Rodolfo Zunino ◽

Paolo Gastaldo

Keyword(s):

Computational Cost ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

Computational Costs ◽

Advantages And Disadvantages ◽

Complex Information ◽

Large Sets ◽

Learning Techniques ◽

Feature Extractor ◽

Overall Performance

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost.

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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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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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A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images

Frontiers in Oncology ◽

10.3389/fonc.2021.737368 ◽

2021 ◽

Vol 11 ◽

Author(s):

Mehdi Astaraki ◽

Guang Yang ◽

Yousuf Zakko ◽

Iuliana Toma-Dasu ◽

Örjan Smedby ◽

...

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Comparative Study ◽

Pulmonary Nodule ◽

Lung Nodule ◽

Fine Tuning ◽

Deep Feature ◽

Feature Based ◽

End To End ◽

Deep Learning Model

ObjectivesBoth radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.MethodsConventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.ResultsThe best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).ConclusionThe end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.

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From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

10.31234/osf.io/xg2uy ◽

2021 ◽

Author(s):

Johannes Janek Daniel Singer ◽

Katja Seeliger ◽

Tim Christian Kietzmann ◽

Martin N Hebart

Keyword(s):

Neural Networks ◽

Poor Performance ◽

Classification Performance ◽

Natural Images ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

Line Drawings ◽

General Utility ◽

Intermediate Layers ◽

Latent Representations

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photos, drawings and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images such as drawings seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.

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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Sensors ◽

10.3390/s21062222 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2222

Author(s):

Jaeyong Kang ◽

Zahid Ullah ◽

Jeonghwan Gwak

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Magnetic Resonance Imaging ◽

Tumor Classification ◽

Support Vector ◽

Deep Convolutional Neural Networks ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Deep Feature

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

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Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Entropy ◽

10.3390/e23121651 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1651

Author(s):

Prabal Datta Barua ◽

Wai Yee Chan ◽

Sengul Dogan ◽

Mehmet Baygin ◽

Turker Tuncer ◽

...

Keyword(s):

High Performance ◽

Feature Vector ◽

Automated Detection ◽

Support Vector ◽

Feature Generation ◽

Learning Techniques ◽

Deep Feature ◽

Retinal Disorders ◽

Feature Extractor ◽

New Framework

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

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