Revolutionizing Skin Cancer Detection with Raspberry PiEmbedded ANN Technology in an Automated Screening Booth
Objective
The objective of the Revolutionary Skin Care Detection System is to develop an AI-based healthcare system that detects skin conditions using YOLOv8 and monitors vital parameters such as heart rate and body temperature in real time. The system aims to provide early skin disease detection, health monitoring, and alert notifications for improved healthcare and user safety.
Abstract
This project introduces an innovative approach to early skin cancer detection using a Raspberry Pi-based automated screening booth integrated with Artificial Neural Network (ANN) technology. The system captures high-resolution skin images via a USB web camera and uses an embedded ANN model to classify potential skin anomalies into keratosis, basal cell carcinoma, melanoma, benign, and nevus categories. A heartbeat sensor and Dallas temperature sensor are included to gather supplementary health indicators, improving screening accuracy. The MCP3008 ADC interfaces analog sensors with the Raspberry Pi for seamless data acquisition. Results are displayed on an LCD screen, and a buzzer alerts users if abnormalities are detected. This self-service screening system offers a cost-effective, portable, and intelligent solution for preliminary skin cancer detection, particularly in remote or underserved areas.
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
- Memory Card
- USB Web Camera
- Heartbeat Sensor
- Dallas Temperature Sensor
- MCP3008 ADC Module
- Buzzer
- LCD Display
- Power Supply
- 12V Adapter
- Connectors
- Arduino IDE
- Raspbian OS
- Embedded C
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
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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.
