Objectives
• Designing an AI-driven diagnostic system that enhances accuracy and reduces diagnostic time. • Ensuring affordability and accessibility of AI-powered healthcare tools in remote and underprivileged areas. • Developing a scalable solution that integrates with existing healthcare frameworks and telemedicine services. • Addressing challenges related to data security, ethical AI usage, and model transparency.
Socio-Economic Benefit
From a social perspective, the system improves healthcare accessibility by enabling early and accurate disease detection, even in areas lacking trained medical professionals. By providing diagnostic support through a low-cost, easily deployable platform, it empowers communities to receive timely healthcare interventions, thereby reducing mortality rates and enhancing overall quality of life. The integration of AI with telemedicine further facilitates remote consultations, overcoming geographical and infrastructural barriers.
Methodologies
The project employs a multi-modal machine learning approach using pre-trained models for different medical conditions. For brain tumor detection, it utilizes a deep learning convolutional neural network (CNN) model built with TensorFlow to analyze MRI images through image preprocessing, resizing to 299x299 pixels, and normalization. For diabetes, heart disease, and Parkinson's disease assessments, it implements traditional machine learning models using scikit-learn algorithms with feature scaling through StandardScaler for numerical data normalization. The application uses Streamlit as the web framework to create an interactive user interface with medical-themed styling and responsive design. Data preprocessing methodologies include feature engineering, data normalization, and sample case generation for demonstration purposes. The system incorporates model caching for performance optimization and includes comprehensive error handling for robust deployment. Each assessment module follows a standardized methodology of data input validation, model prediction, and result interpretation with appropriate medical disclaimers.
Outcome
This project is a multi-page Streamlit web application that uses various machine learning models to offer predictive health assessments for conditions such as brain tumors, diabetes, heart disease, and Parkinson's disease.
Project Team Members
| Registration# | Name |
|---|---|
| CUID-2111-2021 | Ali |
| CUID-2108-2021 | Huzaifa |
| CVID-2472-2021 | Muhammad Waqas |
PROJECT GALLERY
AI-Powered Early Disease Detection: Scalable & Accessible Healthcare Solutions
22 Jun, 2025 06:08 pm
