A comprehensive review of deep learning in lung cancer
Not on the map. Why?
Abstract (arXiv)
To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 59 references that are in the map. First posted on arXiv 31 Jul 2023.
Builds on
11 of 59 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2014Going Deeper with Convolutionscited by 4,231
- 2018Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancercited by 34
- 2017Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissuescited by 16
- 2017Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology imagescited by 8
- 2021Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learningcited by 5
- 2021Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)cited by 5
- 2021Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetescited by 4
- 2019ML for Flood Forecasting at Scalecited by 3
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Citing papers per year: 2023: 1.
1 paper.
- 2023Review of deep learning in healthcarecited by 0
- 2023Review of deep learning in healthcare1 Oct 2023