A Comparison of Image Classification with Different Activation Functions in Balanced and Unbalanced Datasets

When the novel coronavirus (COVID-19) outbreak began to ring alarm bells worldwide, rapid, efficient diagnosis was critical to the emergency response. The limited ability of medical systems and the increasing number of daily cases pushed researchers to investigate automated models. The use of deep neural networks to help doctors make the correct diagnosis has dramatically reduced the pressure on the healthcare system. Promoting the improvement of diagnosis networks depends not only on the network structure design but also on the activation function performance. To identify an optimal activation function, this study investigates the correlation between the activation function selection and image classification performance in balanced or imbalanced datasets. Our analysis evaluates various network architectures for both commonly used and novel datasets and presents a comprehensive analysis of ten widely used activation functions. The experimental results show that the swish and softplus functions enhance the classification ability of state-of-the-art networks. Finally, this thesis distinguishes the neural networks using ten activation functions, analyzes their pros and cons, and puts forward detailed suggestions on choosing appropriate activation functions in future work.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Zhang, Moqi

Chair dc:contributor.committeechair

• Yi, Yang

Committee members dc:contributor.committeemember

• Zeng, Haibo
• Huang, Jia-Bin

• deep learning
• activation function
• COVID-19

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:31449

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/103631