Developed an innovative web-based diabetes detection system using CNN and transfer learning, achieving high accuracy on the Pima Indians dataset. The project successfully implemented novel techniques for transforming tabular medical data into image representations, demonstrating effective application of deep learning in healthcare diagnostics.

Overview

The project addresses the challenge of applying deep learning to small medical datasets through innovative data transformation techniques. By converting tabular patient data into image representations using a novel approach inspired by IGTD, the system leverages transfer learning with pre-trained ResNet models to achieve accurate diabetes detection. The implementation demonstrates the practical application of advanced deep learning techniques in healthcare diagnostics, particularly for datasets with fewer than 1,000 samples.

Technologies

• Deep Learning Framework: PyTorch, TensorFlow
• Web Framework: Flask
• Pre-trained Models: ResNet
• Data Processing: Pandas, NumPy
• Frontend: HTML, CSS, JavaScript
• Backend: Python
• Image Processing: OpenCV, Pillow
• Model Deployment: Flask-RESTful

Implementation Details

1. Data Transformation System

• Developed novel tabular-to-image conversion methodology
• Implemented blocked image representation technique
• Created data preprocessing pipeline
• Established feature normalisation system
• Developed data validation framework

2. Model Architecture

• Implemented transfer learning with ResNet
• Created custom CNN layers for classification
• Developed model fine-tuning pipeline
• Implemented early stopping mechanism
• Created model evaluation framework

3. Web Application Development

• Created responsive Flask-based interface
• Implemented real-time prediction system
• Developed user input validation
• Created secure data handling system
• Implemented RESTful API endpoints

4. Performance Optimisation

• Implemented model compression techniques
• Developed efficient data processing pipeline
• Created caching mechanisms
• Optimised inference speed
• Implemented batch prediction system

Key Results

• Achieved high accuracy on Pima Indians dataset
• Successfully transformed tabular data to images
• Implemented efficient transfer learning system
• Developed user-friendly web interface
• Created comprehensive reporting system
• Demonstrated scalability with small datasets
• Established reliable prediction framework

Skills Gained

• Deep learning model development
• Healthcare analytics and Medical data processing
• Web application development
• Transfer learning implementation
• Data transformation techniques
• Model deployment
• API development
• User interface design

Impact

The application provides an accessible tool for diabetes risk assessment, demonstrating the potential of deep learning in medical diagnostics. The novel approach to handling small medical datasets through image transformation opens new possibilities for applying deep learning in healthcare scenarios with limited data availability. The project’s success in achieving high accuracy while maintaining user-friendly access showcases the practical application of advanced AI techniques in healthcare.