Build classification models to predict whether the cancer type is Malignant or Benign.

This repository contains some comprehensive approaches for the purpose of classifying breast cancer tissue using whole slide images (WSIs).

Open Source Breast Cancer Research for Summer Research Program ASIME 2024 @ Adelphi University, New York. Data from the UV Irvine Machine Learning Repository.

build and evaluate Logistic Regression and SVM models to predict the diagnosis of breast cancer based on provided features

A web-based application for classifying breast ultrasound images into benign, malignant, or normal categories. Built with Flask for the web interface and TensorFlow for image classification. Dockerized for easy deployment and consistent environments. Includes comprehensive documentation, sample images, and a demonstration video

A comprehensive machine learning-based web app for predicting multiple diseases from medical data.

Breast Cancer Prediction Project

This contains all the project in which i have used KNN to Predict output

Breast Cancer Classification using machine learning algorithms to analyze and predict the likelihood of breast cancer from diagnostic data, including feature extraction and model evaluation

This project, part of a remote internship at Nexus Info, focuses on using machine learning to analyze and predict breast cancer. The project involves developing and evaluating models to accurately diagnose the disease from various medical parameters.

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.

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%

Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)

This project leverages deep learning techniques to assist in the early detection of breast cancer using ultrasound images. By training a Convolutional Neural Network on a dataset of breast ultrasound images, the model can classify tumors as benign or malignant with high accuracy. The project includes a user-friendly web interface bu

This a Deep Learning and Machine Learning project performed on Wisconsin Breast Cancer dataset.

Using Machine Learning Methods to predicting Breast Cancer

This repository contains a comparison of different Naive Bayes classifiers (Bernoulli, Gaussian, and Multinomial) for predicting benign and malignant cancer cases. The project includes confusion matrices for each classifier to evaluate their performance.

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.

A web app developed for my Bachelor's Thesis to compare classifiers for detecting malignant tumors from fine needle aspiration biopsies. It includes classifier metrics, SHAP analysis for feature contributions, a classifier comparison tool, and a project overview slideshow.

A machine learning project to predict breast cancer using logistic regression. This project includes data preprocessing, feature scaling, model training, and evaluation, based on a guided project from Coursera.