histopathologic_cancer_detector：CNN组织病理学肿瘤识别码

<h3 align="center"> <img src="assets/histopathologic_cancer_detector_icon_web.png" width="300"> </h3> # Histopathologic Cancer Detector Python Jupyter Notebook leveraging **Transfer Learning** and **Convolutional Neural Networks** implemented with **Keras**. Part of the [Kaggle competition](https://www.kaggle.com/c/histopathologic-cancer-detection). Submitted [Kernel](https://www.kaggle.com/greg115/histopathologic-cancer-detector-lb-0-958) with 0.958 LB score. Check out corresponding Medium article: [Histopathologic Cancer Detector - Machine Learning in Medicine](https://towardsdatascience.com/histopathologic-cancer-detector-finding-cancer-cells-with-machine-learning-b77ce1ee9b0a) ## Data **Dataset:** [Link](https://www.kaggle.com/c/histopathologic-cancer-detection/data) **Description:** Binary classification whether a given histopathologic image contains a tumor or not. **Training:** 153k (0.9) images **Validation:** 17k (0.1) images **Testing:** 57.5k images ## Model <h3> <img src="assets/model_plot.png" width="500"> </h3> __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 96, 96, 3) 0 __________________________________________________________________________________________________ xception (Model) (None, 3, 3, 2048) 20861480 input_1[0][0] __________________________________________________________________________________________________ NASNet (Model) (None, 3, 3, 1056) 4269716 input_1[0][0] __________________________________________________________________________________________________ global_average_pooling2d_1 (Glo (None, 2048) 0 xception[1][0] __________________________________________________________________________________________________ global_average_pooling2d_2 (Glo (None, 1056) 0 NASNet[1][0] __________________________________________________________________________________________________ concatenate_5 (Concatenate) (None, 3104) 0 global_average_pooling2d_1[0][0] global_average_pooling2d_2[0][0] __________________________________________________________________________________________________ dropout_1 (Dropout) (None, 3104) 0 concatenate_5[0][0] __________________________________________________________________________________________________ dense_1 (Dense) (None, 1) 3105 dropout_1[0][0] ================================================================================================== Total params: 25,134,301 Trainable params: 25,043,035 Non-trainable params: 91,266 __________________________________________________________________________________________________ ## Training <h3> <img src="assets/training.png" width="500"> </h3> <h3> <img src="assets/validation.png" width="500"> </h3> <h3> <img src="assets/roc.png" width="500"> </h3> ## Results Kaggle score: **0.958** ## Author **Greg (Grzegorz) Surma** [**PORTFOLIO**](https://gsurma.github.io) [**GITHUB**](https://github.com/gsurma) [**BLOG**](https://medium.com/@gsurma) <a href="https://www.paypal.com/paypalme2/grzegorzsurma115"> <img alt="Support via PayPal" src="https://cdn.rawgit.com/twolfson/paypal-github-button/1.0.0/dist/button.svg"/> </a>