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

Yükleniyor...
Küçük Resim

Tarih

2023

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Dicle Üniversitesi Mühendislik Fakültesi

Erişim Hakkı

info:eu-repo/semantics/openAccess

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 k=10) 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.

Açıklama

Anahtar Kelimeler

COVID-19, CNN, CapsNet, K-fold, Leave-one-out

Kaynak

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi

WoS Q Değeri

Scopus Q Değeri

Cilt

14

Sayı

2

Künye

Muhammad, A., Arserim, M. A. ve Türk, Ö. (2023). Compare the classification performances of convolutional neural networks and capsule networks on the Coswara dataset. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 14(2), 265-271