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

Authors : Abdulazız MUHAMMAD, Muhammet Ali ARSERİM, Ömer TÜRK

Pages : 265-271

Doi:10.24012/dumf.1270429

View : 79 | Download : 119

Publication Date : 2023-06-20

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(CNN); and capsule networks insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : COVID 19, CNN, CapsNet, k fold, leave one out