CNNFET: Convolutional neural network feature Extraction Tools
Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. Thi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235271102500055X |
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| _version_ | 1850109340819652608 |
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| author | Huseyin Atasoy Yakup Kutlu |
| author_facet | Huseyin Atasoy Yakup Kutlu |
| author_sort | Huseyin Atasoy |
| collection | DOAJ |
| description | Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. This makes them very popular in signal processing and especially in image processing community. Despite the proven success of these networks, training processes of them are often expensive in terms of time and required hardware capabilities. In this paper, a user-friendly standalone Windows application titled “Convolutional Neural Network Feature Extraction Tools” (CNNFET) is presented. The application consists of tools that extract features from image sets using certain layers of pre-trained CNNs, process them, perform classifications on them and export features for further processing in Matlab or the popular machine learning software Weka. |
| format | Article |
| id | doaj-art-1c6ef38adb09482d99fb6f08dd7103fc |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-1c6ef38adb09482d99fb6f08dd7103fc2025-08-20T02:38:06ZengElsevierSoftwareX2352-71102025-05-013010208810.1016/j.softx.2025.102088CNNFET: Convolutional neural network feature Extraction ToolsHuseyin Atasoy0Yakup Kutlu1Information Technologies Office, Iskenderun Technical University, Hatay, Turkey; Corresponding author.Computer Engineering Department, Iskenderun Technical University, Hatay, TurkeyNeither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. This makes them very popular in signal processing and especially in image processing community. Despite the proven success of these networks, training processes of them are often expensive in terms of time and required hardware capabilities. In this paper, a user-friendly standalone Windows application titled “Convolutional Neural Network Feature Extraction Tools” (CNNFET) is presented. The application consists of tools that extract features from image sets using certain layers of pre-trained CNNs, process them, perform classifications on them and export features for further processing in Matlab or the popular machine learning software Weka.http://www.sciencedirect.com/science/article/pii/S235271102500055XConvolutional neural networksFeature extractionTransfer learning |
| spellingShingle | Huseyin Atasoy Yakup Kutlu CNNFET: Convolutional neural network feature Extraction Tools SoftwareX Convolutional neural networks Feature extraction Transfer learning |
| title | CNNFET: Convolutional neural network feature Extraction Tools |
| title_full | CNNFET: Convolutional neural network feature Extraction Tools |
| title_fullStr | CNNFET: Convolutional neural network feature Extraction Tools |
| title_full_unstemmed | CNNFET: Convolutional neural network feature Extraction Tools |
| title_short | CNNFET: Convolutional neural network feature Extraction Tools |
| title_sort | cnnfet convolutional neural network feature extraction tools |
| topic | Convolutional neural networks Feature extraction Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S235271102500055X |
| work_keys_str_mv | AT huseyinatasoy cnnfetconvolutionalneuralnetworkfeatureextractiontools AT yakupkutlu cnnfetconvolutionalneuralnetworkfeatureextractiontools |