Enhanced Breast Cancer Diagnosis Using Machine Learning on Patient Data and Deep Learning | Python

Enhanced Breast Cancer Diagnosis Using Machine Learning on Patient Data and Deep Learning | Python IEEE Final Year Project 2024 - 2025.
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🚀IEEE Base Paper Title: Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm.
📌Our Proposed Project Title: Enhanced Breast Cancer Diagnosis Using Machine Learning on Patient Data and Deep Learning.
💡Implementation: Python.
🔬Algorithm / Model Used: Stacking Classifier, CatBoost Classifier & DenseNet201 Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

📘Project Abstract:
The "Enhanced Breast Cancer Diagnosis Using Machine Learning on Patient Data and Deep Learning" project introduces a robust, dual-modality diagnostic system that leverages both patient data and histopathology image analysis for improved breast cancer detection and classification. Developed in Python, with a user-friendly front end built using HTML, CSS, and JavaScript, the system is deployed via the Flask web framework, providing a comprehensive interface for clinical diagnostics. This hybrid approach combines traditional Machine Learning with Deep Learning techniques, enabling accurate predictions from both structured patient data and biopsy images.

📍REFERENCE:
AMREEN BATOOL AND YUNG-CHEOL BYUN, “Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm”, in IEEE Access, vol. 12, pp. 12869-12882, 2024.

❓Frequently Asked Questions:
1. What is the main purpose of this project?
2. How does this project differ from existing systems?
3. Which machine learning and deep learning models are used in this project?
4. What accuracy has been achieved by the machine learning and deep learning models in this project?
5. What datasets were used in this project?
6. How does the system ensure the accuracy of its predictions?
7. Is the system intended to replace radiologists and pathologists?
8. What are the primary components of the system architecture?
9. Can this system be deployed in real-world healthcare environments?
10. What are the benefits of using both machine learning and deep learning for this diagnosis system?

🏷️tags:
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