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

PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

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.

Using Python Machine Learning Supervised and Unsupervised Learning Models on Jupyter Notebook

Rule-based expert system for prediction of a patient's survival within 5 years of undergoing breast cancer surgery & its comparison with ML classification techniques.

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains where spatial attention is produced by biomarkers and tumor cells

Using Python Machine Learning Supervised and Unsupervised Learning Models on Jupyter Notebook

NeuralScan is an intelligent medical analysis system built with Python and machine learning. It integrates multiple ML models for breast cancer and diabetes prediction, featuring an analytical dashboard, model selection control, and a smart guidance module for research and educational use.

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Multiple Disease Prediction System is a supervised Machine Learning Model in Python. I have also deployed this machine learning web app using Streamlit. This web app can predict the diseases of a human such as Diabetes, heart disease, Parkinson's and Breast Cancer using Machine Learning model.

This web-based Breast Cancer Prediction system utilizes machine learning models to predict whether a tumor is malignant (cancerous) or benign (non-cancerous) based on user-provided medical data. The system visualizes the prediction results with an interactive pie chart showing the probability of Cancer

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.

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A user-friendly Breast Cancer Prediction System using Streamlit and Django. This project calculates thresholds for tumor classification features to predict malignancy or benignity, and securely stores predictions in a Django backend.

The code performs breast cancer classification using logistic regression. It loads data from a CSV, preprocesses it, visualizes using seaborn, trains a model, and predicts tumor type. A tkinter GUI allows input of tumor features for real-time prediction.

A Prediction System that detects the presence of Cancerous Cells particularly for Breast Cancer and classify as Malignant or Benign tumor. This prediction system has been created using Machine learning algorithms like Logistic Regression, Decision and Random Forest. The dataset has been taken from Kaggle.

End-to-end ML system for breast cancer diagnosis with 97% accuracy. Includes model comparison, hyperparameter tuning, web interface, REST API, and visualization dashboard.

This is a breast cancer prediction system implemented in Python with a Tkinter GUI

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Breast Cancer Prediction System using Machine Learning Algorithms

Predicting Chances of Breast Cancer using Gen AI, Machine Learning. Building Interactive Interface, Backend using Python and Machine Learning with real- time ChatBot.

Developed a Python-based ML system to predict breast cancer mortality status and survival months from clinical data. Built end-to-end notebooks for preprocessing, classification, hyperparameter tuning, ensemble models, and Decision Tree regression to support accurate predictive analysis from healthcare data.

An Explainable Multimodal AI System for Breast Cancer Subtype Prediction

Developing a Breast Cancer Prediction System Using Machine Learning Techniques

Breast Cancer Detection and Treatment Prediction system using machine learning and deep learning techniques.

Clinically inspired multi-modal breast cancer prediction system with transparent, reliable, and interpretable AI insights.

● Developed a prediction system for Diabetics, Heart, Breast Cancer, Parkinson’s Diseases. ● Implemented Streamlit for deploying Prediction System into web application.

AI-powered breast cancer prediction system using Logistic Regression, Flask REST API + React frontend deployed on Vercel.