Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

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Breast cancer detection using YOLO and image processing

This Project for Research paper Amity University Mohali . This project contains 100 gb of dataset including csv files , github don't support to upload 100 gb straight, if dataset is needed connect me on LinkedIn. Id - aaryan2931

Building a breast cancer detection system using AI

AI-based breast cancer detection system using YOLOv8 and ResNet50 for lesion localization and classification

An AI-powered system for Breast Cancer detection using Deep Learning (VGG16) and a modern GUI with CustomTkinter.

AI-powered web application designed to assist in early breast cancer detection by analyzing medical images using machine learning techniques. The system allows users to upload diagnostic images and receive predictions indicating whether the tumor is benign or malignant.

Developed an AI-based breast cancer detection system using CycleGAN for stain color normalization and DPN68-A for feature extraction. Improved classification accuracy on histopathology images using precision, recall, and F1-score, supporting early diagnosis and clinical decision-making.

AI-Based Disease Detection & Guidance System is a healthcare application that predicts diseases like heart disease, diabetes, breast cancer, and brain tumors using machine learning and deep learning. It provides real-time predictions, MRI analysis with explainability, and personalized health recommendations.

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This Project uses the Wisconsin (original) dataset to get the result. This project uses various algorithms: Classification & Regression Trees (CART) Linear Support Vector Machine (SVM) Gaussian Naive Bayes (NB) K-Nearest Neighbours (KNN) Random Forest (RF).

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AI HealthCare - Breast Cancer Detection System with Microwave Imaging

AI Powered Breast Cancer Detection System from Ultrsound Images

An Agentic AI system for Breast Cancer Detection and Care

An Agentic AI system for breast cancer detection and prediction.

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AI system for breast cancer detection using sklearn breast_cancer dataset and Decision Trees/K-nn as classifiers

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This is breast cancer detection system using deep learning project (AI) project

AI-powered breast cancer detection system using machine learning (96-98% accuracy)

AI-Driven Breast Cancer Detection and Clinical Task Automation Using a Web-Based Decision Support System

Explainable AI system for breast cancer detection using CNN, Grad-CAM and FastAPI

Mutlimodal AI system combining clinical features and mammographic imaging for breast cancer detection.

A Human-in-the-Loop AI system for breast cancer detection using Ultrasound images.

AI-powered system for early breast cancer detection using machine learning and medical imaging.