AI Breast cancer detection using InBreast, CBIS-DDSM, MIAS mammography image datasets

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.

The repository provides the implementation of Perceptron model and Mp neuron model and a Breast cancer detection model using both the models

Medical Image processing project.

DiagnoSys is a comprehensive web application that provides advanced detection and analysis for various health conditions. This project leverages state-of-the-art machine learning algorithms to detect and diagnose COVID-19, Alzheimer's disease, breast cancer, and pneumonia using X-ray and MRI datasets.

This project uses mammograms for breast cancer detection using deep learning techniques.

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

This is a machine learning project for early diagnosis of breast cancer using a RandomForest classifier. It includes code for data loading, preprocessing, model training, and evaluation metrics. The project was created as an academic assignment to demonstrate the use of RandomForest in healthcare analytics.

Breast Cancer Detection Using Machine Learning Project Code, PPT, Synopsis, Report and Video Explanation

Machine Learning Class Project - Spring '22: Breast Cancer Prediction from Mammograms using Autoencoders to solve class imbalance problem

Computer vision to assist with early detection of breast cancer.

This project uses mammograms for breast cancer detection using deep learning techniques.

Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.

Code for published article in PLOS ONE. Breast cancer detection in mammograms using DL techniques. Contains source code for data and methods used.

The implementation of our pricai18 paper "Multiple Visual Fields Cascaded Convolutional Neural Network for Breast Cancer Detection."

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps reduce the number of premature deaths. In the study, I am working on creating a convolutional neural network capable of identifying tumor areas within medical images (which were taken with ultrasound).

Breast Cancer detection study with Pycaret library.

Breast cancer detection using Python involves the use of machine learning algorithms to analyze breast cancer data and predict whether a given case is malignant (cancerous) or benign (non-cancerous). Python provides a variety of libraries for building breast cancer detection models.