Classification of Breast Cancer diagnosis Using Support Vector Machines

Classification of Breast Cancer diagnosis Using Support Vector Machines

A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.

Developed using Python and Google Collab Notebook, this project leverages a Simple Multilayer Perceptron Neural Network (Feed Forward model) for breast cancer prediction. It utilizes the sklearn library for , and model evaluation. The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, Accuracy-95%

The repository provides code for running inference with different breast cancer models, links for downloading the trained model checkpoints, and example notebooks on how work with a DICOM pipeline.

This repository showcases a collection of practical machine learning projects, utilizing diverse datasets for tasks like classification, regression, and forecasting. With Jupyter notebooks detailing techniques from decision trees to neural networks, this project demonstrates my ability to solve real-world problems through data-driven insights.