ResNet‐Attention model for human authentication using ECG signals

Hammad, Mohamed; Pławiak, Paweł; Wang, Kuanquan; Acharya, U. Rajendra

doi:10.1111/exsy.12547

Cited by 91 publications

(73 citation statements)

References 53 publications

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“…It is a supervised learning algorithm that can be employed for regression and classification problems by finding a hyperplane that splits the features into diverse domains [52]. SVM called Support Vector Classification (SVC) when employed for classification problems and called Support Vector Regression (SVR) when employed for regression problems.…”

Section: Svm Classifiermentioning

confidence: 99%

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

165

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Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECGbased CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

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Section: Svm Classifiermentioning

confidence: 99%

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

165

View full text Add to dashboard Cite

show abstract

“…For example, [32], [33] use a genetic hierarchical network and SVM to predict credit score. [34], [35] use a deep neural network to evaluate and diagnose the electrocardiogram signal. [36] uses ResNet networks to recognize sensor signals.…”

Section: ) the Applications And High-performance Implementations Of mentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in environmental monitoring and protection, but it is often encountered with small datasets due to the difficulty of EM image collection. To this end, we propose an Enhanced Framework of GANs (EF-GANs) that combines geometric transformation methods and GANs for EM image augmentation. First of all, the color of an EM image has an insignificant impact on its class label, based on this fact, we perform color space augmentation to the original EM images. Secondly, we train EF-GANs with augmented EM images to generate utterly new EM images. Finally, we rotate the generated samples in various directions to obtain a more natural performance. In this study, we use VGG16 and ResNet50 networks to evaluate the proposed EF-GANs on 21 different types of EMs (420 EM images). It is observed that the average precision (AP) of VGG16 increases between 4.5% and 84.1% in 20 EM classes and one class remains unchanged. The AP of Resnet50 rises between 8.7% and 38.7% in 12 EM classes and reaches 100% in two EM classes. Furthermore, to reflect the generalization performance of EF-GANs, we employ an utterly new EM image dataset (630 EM images) to test the previous VGG16 networks. We select the VGG16 networks with original and optimal settings for all the EM classes, and for testing, optimal settings for a single EM class is considered. In the 20 of 21 one-vs-rest EM image classification tasks, the AP of VGG16 increases between 1.66% and 88.1%. The results demonstrate that the proposed EF-GANs can achieve outstanding performance in augmenting single EM images with high quality and resolution, thus, to improve the APs of EM image classification.

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“…Recently, the convolutional neural network (CNN) has been employed in visual classification system [26]- [29]. In the classification of breast cancer histopathology images, the number of samples is small and the size of images is large, which makes it difficult or even impossible to train a deep learning model based on CNN.…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Man

Yang

2020

IEEE Access

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Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained highresolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best F 1 score of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN. INDEX TERMS Breast cancer histopathological images, densely connected convolutional networks, discriminative patches, generative adversarial networks, image classification

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ResNet‐Attention model for human authentication using ECG signals

Cited by 91 publications

References 53 publications

A Multitier Deep Learning Model for Arrhythmia Detection

A Multitier Deep Learning Model for Arrhythmia Detection

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

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