Healthcare_Diagnostic_System_Proposal.pptx

graduation project proposal

1. Healthcare Diagnostic System Graduation Project Proposal

2. Project Overview This project involves developing an AI-driven healthcare diagnostic system that combines image analysis with patient data. The goal is to enhance diagnostic accuracy and provide personalized treatment recommendations, improving patient care.

3. Objectives Develop a computer vision model to detect and classify medical conditions. Integrate patient data with image analysis for comprehensive diagnostics. Enhance diagnostic accuracy through combined analysis. Provide personalized treatment recommendations based on integrated data.

4. Project Scope Image Processing and Analysis: Develop and train a CNN model, use transfer learning. Data Integration and Analysis: Aggregate patient data, develop a data fusion framework. System Development: Create a user interface, test the system with real or simulated data.

5. Methodology Data Collection: Source medical images and patient data. Image Analysis: Develop and train a CNN model using Python and machine learning libraries. Data Integration: Combine structured and unstructured data for comprehensive analysis. System Development: Design the system architecture, develop the user interface, and test the system.

6. Expected Outcomes Enhanced Diagnostic Accuracy: Improved accuracy by integrating image analysis with patient data. Personalized Treatment Plans: Tailored recommendations based on comprehensive data analysis. Feasibility Demonstration: Showcasing the integration of AI into healthcare diagnostics.

7. Timeline Phase 1: Research and Data Collection - 1 Month Phase 2: Image Analysis Development - 2 Months Phase 3: Data Integration and Analysis - 2 Months Phase 4: System Development and Testing - 2 Months Phase 5: Final Evaluation and Reporting - 1 Month

8. Budget and Risk Management Budget: Minimal, focused on software (Python, TensorFlow), hardware (personal computer), and academic resources. Risk Management: Data privacy (use synthetic/public data), model performance (regular validation), time management (adherence to timeline).

9. Conclusion This project will contribute to AI integration in healthcare by developing a system that combines image analysis with patient data. Despite being a solo project, it aims to demonstrate the feasibility and potential impact of AI-driven diagnostics.

10. Contact Information For more information, please contact: Omar Abadi [email protected] 01015642330