This repository implements an Explainable Boosting Machine (EBM) model for breast cancer classification using scikit-learn and interpret. The project includes data preprocessing, model training, accuracy evaluation, and feature importance visualization.

Official Pytorch implementation of MICCAI 2024 paper (early accept, top 11%) Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

Project on Neural Networks for Nanoparticle Breast Cell Classification - Machine Learning (MEng), supervised by Prof. C. Sansone and Eng. M. Gravina(2024)

AWS S3 & Sentiment Analysis, Basic Plotting with Matplotlib, & Supervised Learning & Machine Learning with Sklearn.

Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.

The aim is to categorise and accurately identify the types and subtypes of breast cancer

Bu projede Breast Cancer data veri seti kullanılarak KNN (K Nearest Neighbor) SVM (Support Vector Machine) Naive Bayes algoritmalarıyla eğitim yapılmıştır. Eğitimde Gridsearch kullanılarak en optimum parametreler bulunmuştur.

Analysis of RNA-Seq data for approximately 3500 breast tumors from the SCAN-B project to investigate differential splicing patterns between molecular subtypes and clinical phenotypes

Breast cancer detection using machine learning classification is a project where you build a model to identify whether a given set of medical features indicates the presence of breast cancer. This project involves using a labeled dataset of medical records, where each record is classified as either indicating breast cancer or not.

In this repository, I implemented the deep learning classifier introduced in the paper "Deep Learning to Improve Breast Cancer Detection on Screening Mammography" using PyTorch.

HRadNet: A Hierarchical Radiomics-based Network for Multicenter Breast Cancer Molecular Subtypes Prediction, TMI

Scikit-Learn Supervised Machine Learning for Breast Cancer Binary Classification

SWSSL - Sliding window-based self-supervised learning for anomaly detection in high-resolution images (IEEE Trans. on Medical Imaging 2023)

Multiple Disease Prediction System

Group Project BAI-2023

Interpretable breast cancer classification using convolutional neural networks on mammographic images

The comparison of various machine learning algorithms on breast cancer detection dataset has been done.

Using Neural Networks to classify Malignant and Benign tumors. Exploring Tensorflow and Deep Learning.

Harnessing Deep Learning for Enhanced Mammogram Analysis

A project that I have created while participating in AI ​​Indonesia mentorship