The Potential of Convolutional Neural Networks for Cancer Detection
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913127601274880 |
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| author | Molaeian, Hossein Karamjani, Kaveh Teimouri, Sina Roshani, Saeed Roshani, Sobhan |
| author_facet | Molaeian, Hossein Karamjani, Kaveh Teimouri, Sina Roshani, Saeed Roshani, Sobhan |
| contents | Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17155 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Potential of Convolutional Neural Networks for Cancer Detection Molaeian, Hossein Karamjani, Kaveh Teimouri, Sina Roshani, Saeed Roshani, Sobhan Computer Vision and Pattern Recognition Machine Learning Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare. |
| title | The Potential of Convolutional Neural Networks for Cancer Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.17155 |