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