A Comparison of Image Classification with Different Activation Functions in Balanced and Unbalanced Datasets
| dc.contributor.author | Zhang, Moqi | en |
| dc.contributor.committeechair | Yi, Yang | en |
| dc.contributor.committeemember | Zeng, Haibo | en |
| dc.contributor.committeemember | Huang, Jia-Bin | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2021-06-05T08:02:44Z | en |
| dc.date.available | 2021-06-05T08:02:44Z | en |
| dc.date.issued | 2021-06-04 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | When the novel coronavirus (COVID-19) outbreak began to ring alarm bells worldwide, the rapid, efficient diagnosis was critical to the emergency response. The manual diagnosis of chest X-rays by radiologists is time and cost-consuming. Compared with traditional diagnostic technology, the artificial intelligence medical system can simultaneously analyze and diagnose hundreds of medical images and speedily obtain high precision and high-efficiency returns. As we all know, machines are brilliant in learning new things and never sleep. Suppose machines can be used to replace human beings in some positions. In that case, it can significantly relieve the pressure on the medical system and buy time for medical practitioners to concentrate more on the research of new technologies. We need to know that the critical decision unit of the intelligent diagnosis system is the activation function. Therefore, this work provides an in-depth evaluation and comparison of the traditional and widely used activation functions with the emerging activation functions, which helps to improve the accuracy of the most advanced diagnostic model on the COVID-19 image dataset. Besides, the results of this study also summarize the cons and pros of using various neural functions and provide many suggestions for future work. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:31449 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103631 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | deep learning | en |
| dc.subject | activation function | en |
| dc.subject | COVID-19 | en |
| dc.title | A Comparison of Image Classification with Different Activation Functions in Balanced and Unbalanced Datasets | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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