دورية أكاديمية
Application of Deep Learning in Cancer Prognosis Prediction Model
| العنوان: | Application of Deep Learning in Cancer Prognosis Prediction Model |
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| المؤلفون: | Heng Zhang MS, Qianyi Xi MS, Fan Zhang MS, Qixuan Li MS, Zhuqing Jiao PhD, Xinye Ni PhD |
| المصدر: | Technology in Cancer Research & Treatment, Vol 22 (2023) |
| بيانات النشر: | SAGE Publishing, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1533-0338 15330338 |
| Relation: | https://doaj.org/toc/1533-0338 |
| DOI: | 10.1177/15330338231199287 |
| URL الوصول: | https://doaj.org/article/e10d45abf17a4670a3ecbfedbd0279e7 |
| رقم الأكسشن: | edsdoj.10d45abf17a4670a3ecbfedbd0279e7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 15330338 |
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| DOI: | 10.1177/15330338231199287 |