Compare the classification performances of convolutional neural networks and capsule networks on the Coswara dataset

Abdulazız Muhammad; Muhammet Ali Arserim; Ömer Türk

Makale detayları

Compare the classification performances of convolutional neural networks and capsule networks on the Coswara dataset

Yazarlar:

Abdulazız Muhammad | Muhammet Ali Arserim | Ömer Türk

Yayıncı:

Dicle University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.14 Issue 2, pp.265-271

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291726

pISSN:

1309-8640

Anahtar kelimeler:

Özet:

 Since the beginning of the COVID-19 pandemic, researchers have developed numerous machine learning models to distinguish between positive and negative COVID-19 sounds. The aim of this study is to compare the classification performances of convolutional neural networks CNN and capsule networks CapsNet on the Coswara dataset, which includes 1404 healthy subjects and 522 COVID-19 positive subjects, each containing nine different types of sounds. The dataset was preprocessed by using oversampling and normalization techniques after feature extraction. k-fold cross-validation was used where k10 to train and evaluate the models. The CNN classifiers achieved a 94 ACC, while the CapsNet classifiers achieved an 90 ACC. Furthermore, when using leave-one-out cross-validation, the CNN classifier achieved an ACC of 99. we also compared the performance of the CNN and CapsNet networks on the Coswara dataset without preprocessing. Without oversampling techniques, the CNN classifiers achieved an 93 ACC, compared to 54 for the CapsNet classifiers. When normalization techniques were not applied, the CNN classifiers achieved an 86 ACC, while the CapsNet classifiers achieved a 26 ACC.

 Since the beginning of the COVID-19 pandemic, researchers have developed numerous machine learning models to distinguish between positive and negative COVID-19 sounds. The aim of this study is to compare the classification performances of convolutional neural networks CNN and capsule networks CapsNet on the Coswara dataset, which includes 1404 healthy subjects and 522 COVID-19 positive subjects, each containing nine different types of sounds. The dataset was preprocessed by using oversampling and normalization techniques after feature extraction. k-fold cross-validation was used where k10 to train and evaluate the models. The CNN classifiers achieved a 94 ACC, while the CapsNet classifiers achieved an 90 ACC. Furthermore, when using leave-one-out cross-validation, the CNN classifier achieved an ACC of 99. we also compared the performance of the CNN and CapsNet networks on the Coswara dataset without preprocessing. Without oversampling techniques, the CNN classifiers achieved an 93 ACC, compared to 54 for the CapsNet classifiers. When normalization techniques were not applied, the CNN classifiers achieved an 86 ACC, while the CapsNet classifiers achieved a 26 ACC.

 Since the beginning of the COVID-19 pandemic, researchers have developed numerous machine learning models to distinguish between positive and negative COVID-19 sounds. The aim of this study is to compare the classification performances of convolutional neural networks CNN and capsule networks CapsNet on the Coswara dataset, which includes 1404 healthy subjects and 522 COVID-19 positive subjects, each containing nine different types of sounds. The dataset was preprocessed by using oversampling and normalization techniques after feature extraction. k-fold cross-validation was used where k10 to train and evaluate the models. The CNN classifiers achieved a 94 ACC, while the CapsNet classifiers achieved an 90 ACC. Furthermore, when using leave-one-out cross-validation, the CNN classifier achieved an ACC of 99. we also compared the performance of the CNN and CapsNet networks on the Coswara dataset without preprocessing. Without oversampling techniques, the CNN classifiers achieved an 93 ACC, compared to 54 for the CapsNet classifiers. When normalization techniques were not applied, the CNN classifiers achieved an 86 ACC, while the CapsNet classifiers achieved a 26 ACC.

 Since the onset of the COVID-19 pandemic, numerous machine learning models have been developed to classify and distinguish COVID-19 positive sounds from egative ones. The aim of this study is to compare the classification performances of convolutional neural networks and capsule networks on the Coswara dataset. The dataset was collected using a website application and contains 1404 and 522 healthy COVID-19 positive subjects. Each subject contains nine different types of sounds. After feature extraction, the dataset was preprocessed by applying oversampling SOMTE and normalization MinMax Scaler techniques. K-fold cross-validation was used to train and evaluate the models. The CNN classifiers achieved an AUC of 90, while the CapsNet classifiers achieved an AUC of 86. Finally, when leave-one-out cross-validation was used, the CNN classifier achieved an AUC of 99. In addition, we also compared the performance of the CNN and CapsNet networks without applying any preprocessing techniques to the Coswara dataset. The CNN classifiers achieved an AUC of 88, while the CapsNet classifiers achieved an AUC of 50 without applying oversampling techniques. Moreover, the CNN classifiers achieved an AUC of 81, while the CapsNet classifiers achieved an AUC of 55 without applying normalization techniques.

Makale detayları

Compare the classification performances of convolutional neural networks and capsule networks on the Coswara dataset

Yazarlar:

Abdulazız Muhammad | Muhammet Ali Arserim | Ömer Türk

Yayıncı:

Dicle University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.14 Issue 2, pp.265-271

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291726

pISSN:

1309-8640

COVID-19

CNN

CapsNet

k-fold

leave-one-outCOVID-19

CNN

CapsNet

k-fold

leave-one-out

Sayı

Makale detayları

Yazarlar: Abdulazız Muhammad | Muhammet Ali Arserim | Ömer Türk

Yayıncı: Dicle University

Ana Başlıklar: Derin Öğrenme

Dergi: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.14 Issue 2, pp.265-271

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001291726

pISSN: 1309-8640

Sayı

Yazarlar:

Abdulazız Muhammad | Muhammet Ali Arserim | Ömer Türk

Yayıncı:

Dicle University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.14 Issue 2, pp.265-271

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291726

pISSN:

1309-8640