This project demonstrates the use of deep learning techniques to classify breast cancer tumors as malignant or benign using clinical diagnostic data.
A neural network model is trained on real medical measurements to identify complex patterns that support early and accurate diagnosis.
The system is built using Python and modern machine learning libraries and follows a complete end to end workflow including data preprocessing model training evaluation and prediction.
The goal of the project is to showcase how artificial intelligence can assist in healthcare analytics and medical decision support systems.
What This Project Does
The model analyzes thirty numerical features derived from digitized images of breast tissue samples.
These features include measurements related to tumor shape texture smoothness and structural irregularities.
Based on these inputs the neural network predicts whether the tumor is malignant or benign.
This approach helps demonstrate how data driven systems can support early cancer detection and improve diagnostic reliability.
Key Highlights
- Uses a neural network built with Keras Sequential API
- Trained on the Breast Cancer Wisconsin dataset from sklearn
- Includes proper data preprocessing and feature scaling
- Implements model validation to monitor performance
- Converts probability outputs into final class predictions
- Demonstrates a complete predictive inference pipeline
Technologies Used
Python
NumPy and Pandas for data handling
Scikit learn for dataset loading preprocessing and evaluation
Keras and TensorFlow for neural network modeling
Matplotlib for data visualization
Real World Impact
Breast cancer detection is one of the most critical applications of machine learning in healthcare.
This project highlights how neural networks can analyze medical data efficiently and assist healthcare professionals in making informed decisions.
While this model is built for learning and demonstration purposes it reflects real world workflows used in medical AI systems.
Learning Outcomes
Through this project I gained hands on experience with
- Neural network architecture design
- Feature scaling and data preprocessing
- Model compilation and training strategies
- Validation and performance evaluation
- Building predictive systems for real world data