This repository contains a Jupyter notebook for the "Histopathologic Cancer Detection" project, which is part of a Kaggle competition. The goal of this project is to develop algorithms that can accurately identify metastatic cancer from small sections of pathology scans. This involves analyzing a large dataset of high-resolution pathology scan images, labeled for the presence or absence of metastatic tissue, to detect cancerous patterns efficiently.
The dataset used in this project is part of the Kaggle competition and consists of high-resolution pathology scan images. It can be downloaded directly within the notebook using the provided code cells.
- Python 3.x
- Jupyter Notebook or Jupyter Lab
- Required Python libraries: [List here the main libraries required, e.g., NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow/Keras]
Clone this repository to your local machine, then install the required Python packages:
git clone https://github.com/MedGhassen/Histopathologic-Cancer-Detection
cd Histopathologic-Cancer-DetectionTo run the notebook, navigate to the cloned repository's directory and start Jupyter Notebook or Jupyter Lab:
cd Histopathologic-Cancer-Detection
jupyter notebookOpen the Histopathologic_Cancer_Detection.ipynb notebook and run the cells sequentially to reproduce the analysis and the results.
Contributions to this project are welcome. Please fork the repository and submit a pull request with your changes.
MIT