https://github.com/asifr001/breastscannn-deep-learning-for-cancer-classification
A deep learning model built with TensorFlow to classify breast cancer as benign or malignant based on clinical features.
https://github.com/asifr001/breastscannn-deep-learning-for-cancer-classification
breast-cancer classification data-science deep-learning healthcare-ai keras neural-network python tensorflow
Last synced: 5 months ago
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A deep learning model built with TensorFlow to classify breast cancer as benign or malignant based on clinical features.
- Host: GitHub
- URL: https://github.com/asifr001/breastscannn-deep-learning-for-cancer-classification
- Owner: Asifr001
- Created: 2025-06-13T19:12:36.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-06-13T19:32:26.000Z (6 months ago)
- Last Synced: 2025-06-26T03:04:29.099Z (5 months ago)
- Topics: breast-cancer, classification, data-science, deep-learning, healthcare-ai, keras, neural-network, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 79.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# BreastScanNN-Deep-Learning-for-Cancer-Classification
This project demonstrates the development of a deep learning model to classify breast cancer cases as benign or malignant using clinical features. Leveraging the power of TensorFlow and Keras, the model processes real-world breast cancer data, offering predictive insights that can support early detection and diagnosis.
🔍 Key Project Components
Dataset: Breast cancer dataset from sklearn.datasets (also available in Kaggle), containing 30 numerical features related to tumor measurements.
Data Preprocessing:
1) Data cleaning and structure validation
2) Target labeling and distribution analysis
3) Standardization using StandardScaler
Model Architecture:
1) Implemented using Keras Sequential API
2) Includes a flattening layer, a hidden dense layer with ReLU activation, and a final output layer with sigmoid activation
Training & Evaluation:
1) Trained on an 80/20 train-test split
2) Evaluated using accuracy, loss curves, and prediction results
Visualization:
1) Explored feature distributions and target balance
2) Suggestions for future implementation: include ROC curves, confusion matrices, and model interpretability tools like SHAP
💡 Why This Project Stands Out
1) Demonstrates hands-on use of neural networks on structured medical data—a valuable application of deep learning outside of image-based datasets.
2) Cleanly structured and highly readable for educational or real-world use.
3) Excellent starting point for deploying AI in clinical decision support systems.
🚀 Future Enhancements
1) Integrate cross-validation and hyperparameter tuning for improved generalization.
2) Deploy the model as a web API for real-time predictions.
3) Extend analysis with model explainability techniques like SHAP or LIME.
4) Train on a broader dataset to support generalization to diverse populations.