Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques
Objective
The objective of this project is to develop a fully automatic deep learning-based system for brain tumor detection and segmentation from MRI images. The system aims to accurately identify abnormal and normal brain tissues using FAHS-SVM and probabilistic neural networks, improving diagnostic efficiency, reducing reliance on manual analysis, and achieving high accuracy in medical imaging.
Abstract
Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques is an intelligent medical diagnostic system developed to assist in the early detection and classification of brain tumors. The system utilizes Raspberry Pi as the main controller and a USB camera to acquire MRI brain scan images for analysis. A YOLO-based deep learning model is trained using MRI image datasets to identify and classify brain tumors into different categories. The captured MRI images are processed in real time, and the classification results are displayed on an LCD screen for easy monitoring. Python is used for image processing, model training, and inference operations. The proposed system provides a low-cost, portable, and efficient solution for assisting healthcare professionals in brain tumor screening and diagnosis.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
- Raspberry Pi
- USB Camera
- LCD Display
- SD Card
- Power Supply
- 12V Adapter
- Connectors – 30
Software components:
- Raspbian OS
- Python
Learning Outcomes
- Understand Raspberry Pi architecture and GPIO configuration
- Learn how to install and configure Raspbian OS and required Python libraries
- Interface analog sensors with Raspberry Pi using MCP3008 ADC
- Implement image classification using Artificial Neural Networks
- Develop real-time skin analysis using USB camera input
- Build automated health screening systems with display and alert features
- Integrate temperature and heartbeat monitoring in diagnostic systems
- Analyze and interpret classification output for healthcare applications
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
Paper Publishing
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6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
