Analysis of The Role of Deep Learning Models in Image Classification Applications
Image classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for imag...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03013.pdf |
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| author | Li Xiang |
| author_facet | Li Xiang |
| author_sort | Li Xiang |
| collection | DOAJ |
| description | Image classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for image classification, however, have been limited by their slow processing speed, rigidity, and high costs. The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. The paper examines the structure, functionality, advantages, and limitations of CNNs and SVMs in the context of image classification, demonstrating that deep learning-driven classification is now a mainstream research focus. This study highlights the transformative impact of these models and provides insights into their future potential. |
| format | Article |
| id | doaj-art-c2b2fde3070944e0a768349cfb5b6524 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-c2b2fde3070944e0a768349cfb5b65242025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700301310.1051/itmconf/20257003013itmconf_dai2024_03013Analysis of The Role of Deep Learning Models in Image Classification ApplicationsLi Xiang0Shenzhen College of International EducationImage classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for image classification, however, have been limited by their slow processing speed, rigidity, and high costs. The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. The paper examines the structure, functionality, advantages, and limitations of CNNs and SVMs in the context of image classification, demonstrating that deep learning-driven classification is now a mainstream research focus. This study highlights the transformative impact of these models and provides insights into their future potential.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03013.pdf |
| spellingShingle | Li Xiang Analysis of The Role of Deep Learning Models in Image Classification Applications ITM Web of Conferences |
| title | Analysis of The Role of Deep Learning Models in Image Classification Applications |
| title_full | Analysis of The Role of Deep Learning Models in Image Classification Applications |
| title_fullStr | Analysis of The Role of Deep Learning Models in Image Classification Applications |
| title_full_unstemmed | Analysis of The Role of Deep Learning Models in Image Classification Applications |
| title_short | Analysis of The Role of Deep Learning Models in Image Classification Applications |
| title_sort | analysis of the role of deep learning models in image classification applications |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03013.pdf |
| work_keys_str_mv | AT lixiang analysisoftheroleofdeeplearningmodelsinimageclassificationapplications |