Breast Cancer Detection

The Breast Cancer Detection Model is a machine learning-based system designed to classify breast tumors as benign or malignant using medical imaging or clinical data. The model leverages supervised learning algorithms to analyze features such as tumor size, texture, and cell density, enabling early and accurate detection of breast cancer.

Key Features:
Data Processing & Feature Engineering: Cleans and preprocesses medical data, extracting relevant features for analysis.
Machine Learning Algorithms: Uses Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks for classification.
Model Evaluation: Assesses performance with accuracy, precision, recall, F1-score, and ROC-AUC metrics.
Deployment: Integrated with a Flask or FastAPI API for real-time predictions.
This model aids in early diagnosis, improving patient outcomes, and assisting medical professionals in decision-making.

The Breast Cancer Detection Model is a machine learning-based system designed to classify breast tumors as benign or malignant using medical imaging or clinical data. The model leverages supervised learning algorithms to analyze features such as tumor size, texture, and cell density, enabling early and accurate detection of breast cancer.

Key Features:
Data Processing & Feature Engineering: Cleans and preprocesses medical data, extracting relevant features for analysis.
Machine Learning Algorithms: Uses Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks for classification.
Model Evaluation: Assesses performance with accuracy, precision, recall, F1-score, and ROC-AUC metrics.
Deployment: Integrated with a Flask or FastAPI API for real-time predictions.
This model aids in early diagnosis, improving patient outcomes, and assisting medical professionals in decision-making.