Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay
Autor: | Chougrad, Hiba, Zouaki, Hamid, Alheyane, Omar |
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Rok vydání: | 2017 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset. Comment: 6 pages, 2 figures, NIPS ML4H 2017: Machine Learning for Health Workshop at NIPS 2017, Long Beach, CA, United States, December 8, 2017 |
Databáze: | arXiv |
Externí odkaz: |