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

A real-time machine learning-based system for early detection and risk assessment of chronic diseases (Diabetes, Heart Disease, Liver Disease, Lung Disease, Lung Cancer, Breast Cancer).

The multiple disease detection system uses data from multiple patient records to predict if a person has god chance of having a disease. It is capable of detecting breast cancer, diabetes, heart, kidney , liver and Parkinson's disease. Trained multiple models for each disease detection.

这是一个基于YOLOv8目标检测算法的乳腺癌计算机辅助诊断系统，可以实现乳腺癌影像图片的的检测、分割、分类。This is a YOLOv8-based computer-aided diagnosis system for breast cancer that enables detection, segmentation, and classification of mammography images.

Multiple Disease Prediction System: An ML-based tool for early disease detection (Diabetes, Heart, Parkinson’s, Liver, Hepatitis, Lung Cancer, Kidney, Breast Cancer). Uses a Streamlit interface with trained models (.sav, .json) for risk prediction. Includes a Healthcare Chatbot for assistance.

Automated detection and classification system for breast cancer metastases in whole-slide images of histological lymph node section

AI-powered Breast Cancer Detection & Healthcare Platform (2026)

Our project focuses on using machine learning classification algorithms to develop a breast cancer detection system. We gathered a diverse dataset and applied preprocessing techniques, feature selection, and various classification algorithms to train and evaluate our models.

Health Assist web app uses the Streamlit framework to help users identify potential diseases. Simply input your symptoms, and the system will predict whether you have diabetes, heart disease, Breast Cancer or Parkinson's disease. The system is designed to be quick and convenient, promoting early detection and timely medical intervention.

Breast Cancer Detection System

Led a multidisciplinary team in developing a machine learning based breast cancer diagnostic tool to aid in early detection and improve patient outcomes. Gathered a diverse dataset of breast cancer cases, including patient data, from website. Preprocessed and cleaned the data, addressing missing values and standardizing formats.

We propose the Deep Neural Breast Cancer Detection (DNBCD) system, a deep learning-based model for accurate breast cancer detection using the BreakHis-400x and BUSI datasets. It addresses class imbalance with class weighting and uses Grad-CAM for interpretability.

This is a "breast cancer" detection system from thermal images using deep learning especially U-net segmentation and CNN architectural model.

The Breast Cancer Detection System is a sophisticated solution designed to assist in the early detection of breast cancer by leveraging machine learning algorithms. This system is built using Python and incorporates various data science techniques to analyze medical datasets, primarily focusing on identifying malignant and benign tumors.

This project implements a Breast Cancer Detection system using Principal Component Analysis (PCA) for dimensionality reduction and a machine learning model for classification.

A cutting-edge automated system for breast cancer detection from histological images. This hybrid approach leverages radiomic features, Vision Transformers (ViTs), and an attention-based Fully Connected Neural Network (AttentionFCNN) for superior accuracy (99.57%) and efficiency on the BreakHis dataset. A powerful assistive tool for pathologists.

The scope of my project is to develop an automated diagnosis support system for the detection of the exact regions where Invasive Ductal Carcinoma (IDC) is present inside a whole mount slide.  IDC is the most common type of all breast cancers. Breast cancer diagnosis usually consists of several steps, including palpation, mammography or ultrasound imaging and breast tissue biopsy. In the particular case of IDC a very important step consists in grading its aggressiveness. To do so, pathologists usually focus on the regions of the mount sample where IDC is present. The automation of the detection of the exact regions of IDC inside a whole mount slide could help reduce costs and time of the test. As a first step in the development of this project I will train a simple CNN model to classify breast histopathology images.

The Breast Cancer Detection System is an application developed to predict whether an input image represents a breast with cancer or not. It utilizes a deep learning model trained on a dataset of breast images to provide accurate predictions.

Thermal based breast cancer detection system using Machine Learning

No description available

It is a machine learning based project which predict whether patients have cancer or not using data obtained during medical checkup.

Breast Cancer Detection Web Application

🩺 A Flask-based web app for detecting breast cancer using machine learning models.

No description available

Built a deep learning model using ResNet50 to classify breast ultrasound images into benign, malignant, and normal.

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

Breast cancer web based application to detect patient has a malignant cancer or benign using (python - restful api flask - Mysql)

A Flask-based web application that predicts the likelihood of breast cancer using a trained machine learning model on medical diagnostic data. The model is built using scikit-learn and deployed on Render for public access.

Building a breast cancer detection system using AI