| dc.contributor.author | Burugupalli, Mohan | |
| dc.description.abstract | In the recent years, deep learning has shown to have a formidable impact on image classification and has bolstered the advances in machine learning research. The scope of image recognition is going to bring big changes in the Information Technology domain. This paper aims to classify medical images by leveraging the advantages of Transfer Learning over Conventional methods. Three types of approaches are used namely, pre-trained CNN as a Feature Extractor, Feature Extractor with Image Augmentation, and Fine-tuning with Image Augmentation. The best pre-trained network architectures such as VGG16, VGG19, ResNet50, Inception, Xception and DenseNet are used for classification with each being applied to all the three approaches mentioned. The results are captured to find the best combination of pre-trained network and an approach that classifies the medical datasets with a higher accuracy. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Image Classification Using Transfer Learning and Convolution Neural Networks | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2020-08-21T19:54:00Z | |
| dc.date.available | 2020-08-21T19:54:00Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/10365/31517 | |
| dc.subject.lcsh | Diagnostic imaging. | |
| dc.subject.lcsh | Diagnostic imaging -- Digital techniques. | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.subject.lcsh | Machine learning. | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Ludwig, Simone | |
