Skin Disorders Prediction Using Deep Learning for Rural Health Monitoring
Objective
To design and develop a deep learning-based system for prediction of skin disorders to support rural health monitoring. To analyze medical images for accurate disease detection and assist early diagnosis, improving healthcare accessibility and enabling timely treatment in resource-limited areas.
Abstract
This project presents Skin Disorders Prediction Using Deep Learning for Rural Health Monitoring, aimed at improving early diagnosis and healthcare accessibility. The system is developed using a Raspberry Pi integrated with a USB web camera, LCD display, temperature sensor, heartbeat sensor, and pulse sensor. The camera captures images of skin conditions, which are analyzed using deep learning models to classify various skin diseases. In addition to image-based prediction, the system monitors vital health parameters such as body temperature, heart rate, and pulse. The collected data is processed to provide a comprehensive health status of the patient. The results, including disease classification and sensor readings, are displayed on the LCD. This system enables early detection, supports remote healthcare monitoring, and is especially useful in rural areas with limited medical facilities.
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
LCD Display
Temperature Sensor
Heartbeat Sensor
Pulse Sensor
Power Supply
Adapter
Software components:
Python
Rasbian OS
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.
