Classification of Breast Cancer diagnosis Using Support Vector Machines

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

[MICCAI 2024, top 11%] Official Pytorch implementation of Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Machine learning classifier for cancer tissues 🔬

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

Homomorphic Encryption and Federated Learning based Privacy-Preserving

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset

Multi-Instance-Learning to check breast cancer. An implementation of Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification[arXiv:1504.07947] https://arxiv.org/abs/1504.07947

Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.

BreakHist Dataset contains histopathological images of eight types of breast cancer, including four benign cancer and for malignant cancer. In this project, I have trained and fined tuned many of the existing CNN models to get over 80% accuracy in multi-class classification.

Code for Paper: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

[CVAMD 2021] "End-to-End Learning of Fused Image and Non-Image Feature for Improved Breast Cancer Classification from MRI"

Data Modelling and Analysis Coursework - UON. the Classification of Breast Cancer

This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.

Algorithm to segment pectoral muscles in breast mammograms

Breast Cancer classification using deep neural network 🔬

This project utilizes a sophisticated deep learning model trained to classify breast ultrasound images into three categories: benign, malignant, or normal, thus determining the presence of breast cancer.

A Hierarchical Graph V-Net with Semi-supervised Pre-training for Breast Cancer Histology Image Classification" (IEEE TMI)

No description available

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database

This project is to test classification algorithms wrote from scratch in python using only numpy. Algorithms wrote in this project: KNN, Logistic Regression and Naive Bayes classifier.

This is a deep learning project to classify breast cancer (Birads from 1 to 5) from mammography and CEDM Images.

This repository is for the breast cancer classification video on Hello World HD (youtube channel)

A simple neural network built with TensorFlow/Keras to classify tumors as malignant or benign using the Breast Cancer Wisconsin dataset. Includes data preprocessing, model training, evaluation, and result visualization. This project demonstrates how deep learning can assist in medical diagnosis.

🏥 AI-powered breast cancer classification using Logistic Regression with 95% accuracy. Features interactive Gradio web interface for real-time predictions on 30 diagnostic parameters from Wisconsin dataset. Includes comprehensive Jupyter notebooks for model training, evaluation metrics, and deployment-ready architecture for healthcare application.

🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.

Breast cancer classification from Mammogram images using Deep CNN with Keras and TensorFlow.

In this study, my task is to classify tumors into malignant or benign using features obtained from several cell images.