Transfer learning based histopathologic image classification for breast cancer detection

By Erkan Deniz, Abdulkadir Sengür, Zehra Kadiroglu, Yanhui Guo, Varun Bajaj, Ümit Budak. Health Information Science and Systems, 2018

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f4008deb11f6a2ec3d52dde72be48cd53c898a45

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Abeer Saber
2. Mohamed Sakr
3. Osama M. Abo-Seida
4. Arabi Keshk
5. Muhammed Fahri Unlersen
6. K. Sabancı
7. Akif Durdu
8. Huiling Chen

Citations in the corpus

• A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021, methodology): “[89] fine-tuned the last three layers in the AlexNet and VGG16 models to classify breast tumors on the BreaKHis dataset.”
• Brain tumor classification using deep CNN features via transfer learning (2019, unknown): “[9] proposed a classifier for breast cancer on histopathologic images.”
• Deep learning approaches for COVID-19 detection based on chest X-ray images (2020, unknown): “More specifically, the task-dependent layers of the CNN model such as the fully-connected layers and the classification output layer are removed from the network architecture and the remaining layers saved for application to the new classification task (Deniz et al., 2018).”
• CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “Thus, previously learned parameters, especially weights, are transferred to the modified new model [57].”