A novel proposed CNN–SVM architecture for ECG scalograms classification

Özaltın, Öznur; Yeniay, Özgür

doi:10.1007/s00500-022-07729-x

Cited by 29 publications

(12 citation statements)

References 54 publications

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“…The algorithm is generalized and advanced for nonlinear and multi class datasets, dividing them into a high-dimensional feature space with a kernel function. Moreover, SVM can overcome challenges posed by confused datasets and overfitting [19].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

D. S. Aabdalla,

Vasumathi

2024

IJAIA

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Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

D. S. Aabdalla,

Vasumathi

2024

IJAIA

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“…SVM algorithm was introduced by Cortes and Vapnik [18] in 1995 specifically for binary classification tasks. The algorithm has been further developed and expanded to handle multiclass and nonlinear datasets [22]. In this study, SVM was implemented to detect rice species from 106 features extracted from images.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…These metrics include accuracy (Acc), sensitivity (Sens), specificity (Spe), precision (Pre), and F1-Score. The equations for these metrics are provided below [22][23][24]:…”

Section: Performance Metricsmentioning

confidence: 99%

Comparison of Support Vector Machines and ShuffleNet for Detection of Rice Species

2023

Proceeding Book of 1st International Conference on Recent and Innovative Results in Engineering and Technology Icriret 2023

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The objective of the current investigation is to identify the optimal approach for the automated identification of rice species based on images. Two types of datasets have been utilized. The initial dataset comprises a total of 75,000 images, including five different rice species, namely Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset is a balanced set comprising 15,000 images for each type. During this dataset's analysis phase, ShuffleNet, one of the pre-trained architectures in Convolutional Neural Networks (CNNs), was used. The second dataset was created by extracting features from the same rice images. 106 distinct features were acquired, which include morphological, shape, and color features as the main features. The previously extracted features have been analyzed for the classification of rice species using Support Vector Machines (SVM), which have a quadratic kernel function. Moreover, the two datasets have been obtained from the URL https://www.muratkoklu.com/datasets/. Additionally, 5fold cross-validation has been applied for both ShuffleNet and SVM to avoid overfitting. Based on the empirical findings, ShuffleNet has achieved an accuracy rate of 99.8%, while SVM has acquired an accuracy rate of 99.9%. While the results exhibit minimal differences, the optimal algorithm choice may be contingent upon the researcher's level of proficiency in the field.

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“…For this purpose, one of the deep learning algorithms was used to detect monkeypox disease. Nowadays, deep learning algorithms have been widely used, particularly in image classification [9][10][11][12][13][14][15]. When images were classified, generally convolutional neural network (CNN) was utilized as a classifier.…”

Section: Introductionmentioning

confidence: 99%

Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Özaltın

Yeniay

2023

Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.

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Monkeypox has recently become an endemic disease that threatens the whole world. The most distinctive feature of this disease is occurring skin lesions. However, in other types of diseases such as chickenpox, measles, and smallpox skin lesions can also be seen. The main aim of this study was to quickly detect monkeypox disease from others through deep learning approaches based on skin images. In this study, MobileNetv2 was used to determine in images whether it was monkeypox or non-monkeypox. To find splitting methods and optimization methods, a comprehensive analysis was performed. The splitting methods included training and testing (70:30 and 80:20) and 10 fold cross validation. The optimization methods as adaptive moment estimation (adam), root mean square propagation (rmsprop), and stochastic gradient descent momentum (sgdm) were used. Then, MobileNetv2 was tasked as a deep feature extractor and features were obtained from the global pooling layer. The Chi-Square feature selection method was used to reduce feature dimensions. Finally, selected features were classified using the Support Vector Machine (SVM) with different kernel functions. In this study, 10 fold cross validation and adam were seen as the best splitting and optimization methods, respectively, with an accuracy of 98.59%. Then, significant features were selected via the Chi-Square method and while classifying 500 features with SVM, an accuracy of 99.69% was observed.

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A novel proposed CNN–SVM architecture for ECG scalograms classification

Cited by 29 publications

References 54 publications

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

Comparison of Support Vector Machines and ShuffleNet for Detection of Rice Species

Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

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