The Classification of Breast Cancer: The Effect of Hyperparameter Optimisation Towards the Efficacy of Feature-Based Transfer Learning Pipeline-Reference-Cited by-同舟云学术

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The Classification of Breast Cancer: The Effect of Hyperparameter Optimisation Towards the Efficacy of Feature-Based Transfer Learning Pipeline

Published:2023 Issue: Volume: Page:15-19
ISSN:2191-530X
Container-title:Deep Learning in Cancer Diagnostics
language:
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Author:

Arzmi Mohd Hafiz,P. P. Abdul Majeed Anwar,Muazu Musa Rabiu,Mohd Razman Mohd Azraai,Gan Hong-Seng,Mohd Khairuddin Ismail,Ab. Nasir Ahmad Fakhri

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8937-7_3

Reference11 articles.

1. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795. https://doi.org/10.1056/nejmp1500523

2. Singh S, Kumar R (2022) Breast cancer detection from histopathology images with deep inception and residual blocks. Multimed Tools Appl 81:5849–5865. https://doi.org/10.1007/S11042-021-11775-2

3. Wang P, Hu X, Li Y, Liu Q, Zhu X (2016) Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process 122:1–13. https://doi.org/10.1016/j.sigpro.2015.11.011

4. Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific Reports 2017 7:1 7:1–10. https://doi.org/10.1038/s41598-017-04075-z

5. Chand S (2020) A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method. Mach Vis Appl 31:1–10

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis;Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023;2023

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