The Breast Cancer Diagnosis app is a machine learning-powered tool designed to assist medical professionals in diagnosing breast cancer. Using a set of measurements, the app predicts whether a breast mass is benign or malignant. It provides a visual representation of the input data using a radar chart and displays the predicted diagnosis and probability of being benign or malignant. The app can be used by manually inputting the measurements or by connecting it to a cytology lab to obtain the data directly from a machine. The connection to the laboratory machine is not a part of the app itself.
The app was developed as a machine learning exercice from the public dataset Breast Cancer Wisconsin (Diagnostic) Data Set. Note that this dataset may not be reliable as this project was developed for educational purposes in the field of machine learning only and not for professional use.
A live version of the application can be found on Streamlit Community Cloud.
You can run this inside a virtual environment to make it easier to manage dependencies. I recommend using conda to create a new environment and install the required packages. You can create a new environment called breast-cancer-diagnosis by running:
conda create -n breast-cancer-diagnosis python=3.10 Then, activate the environment:
conda activate breast-cancer-diagnosisThen, activate the environment:
conda activate breast-cancer-diagnosisTo install the required packages, run:
pip install -r requirements.txtThis will install all the necessary dependencies, including Streamlit, OpenCV, and scikit-image.
To start the app, simply run the following command:
streamlit run app/main.pyThis will launch the app in your default web browser. You can then upload an image of cells to analyze and adjust the various settings to customize the analysis. Once you are satisfied with the results, you can export the measurements to a CSV file for further analysis.