AI-Powered Cervical Cancer Early Detection

Revolutionizing Healthcare for African Women

Using computer vision analysis of Pap smear images and structured data to democratize expert-level screening

Team Diversity

Kenya | Eswatini | The Gambia | Ghana

Mentor - Peter Ohue

Motivation: A Critical Health Crisis

#1

Leading cause of cancer deaths among African women

90%

Cure rate when detected early

Limited

Access to trained cytopathologists

Current Healthcare Barriers

Limited Specialists: Scarcity of trained cytopathologists in sub-Saharan Africa
Geographic Inequality: Expensive diagnostic equipment concentrated in urban centers
Late-Stage Detection: Most cases discovered when treatment options are severely limited
Resource Constraints: Rural clinics lack access to expert-level screening capabilities

Our Solution: Democratize expert-level cervical cancer screening by making it accessible in remote clinics and resource-constrained settings, combining AI-powered image analysis with demographic risk factors for comprehensive early detection.

Project Feasibility

🏥 Domain Expertise

                Pathologist Collaboration:
                Expert guidance throughout project lifecycle
Clinical validation of AI model predictions
Ensures alignment with diagnostic standards
Realistic healthcare workflow integration

            

💻 Technical Feasibility

Open-Source Tools: TensorFlow, PyTorch, OpenCV, Roboflow
Mobile Deployment: MobileNet architecture with quantization
Offline Capability: Lightweight models for rural/remote areas
Cross-Platform: Mobile and clinic computer compatibility

📊 Data Availability

Existing Pap smear image datasets available
Data augmentation strategies to enhance diversity
Transfer learning with pre-trained models
Federated learning approach for privacy

Target Outcomes & Impact

🎯 Primary Outcomes

↓

Reduced Mortality
Early detection saves lives

↑

Healthcare Access
Reaching underserved communities

💰

Cost Reduction
Prevention over treatment

🌍 Transformational Impact

Early Detection & Reduced Mortality: Timely identification before cancer progression to critical stages
Universal Healthcare Access: AI-powered offline screening for rural and underserved communities
Economic Benefits: Reduced healthcare costs by preventing advanced treatments
Quality of Life: Enable women to maintain roles as mothers, caregivers, and economic contributors
Population Health Intelligence: Insights for targeted prevention strategies and policy development

Vision: A future where no African woman dies from cervical cancer due to lack of access to early detection screening.

Ethical Considerations & Risk Mitigation

⚠️ Identified Risks

Data Privacy Concerns

Sensitive medical imagery and patient information

Limited Training Data

Potential bias and reduced model accuracy

Regulatory Compliance

Different data protection laws across countries

✅ Mitigation Strategies

Federated Learning

Train models without centralizing sensitive data

Data Augmentation

Enhance dataset diversity and reduce bias

Compliance Framework

Informed consent & regulatory adherence

🛡️ Ethical Framework

Data Anonymization: Complete removal of personal identifying information
Informed Consent: Clear communication about data use and AI assistance
Transparency: Open about AI limitations and need for expert validation
Equity: Ensuring the technology benefits underserved populations most

Team Structure & Expertise

Lorraine Chepkemoi (Kenya)
Project Coordinator
MSc Computer Science, ML anomaly detection research

Khanyisile Tapiwa Magagula (Eswatini)
Computer Vision Expert
Computer Science graduate, CV in Agriculture

Simeon Krah (Ghana)
Data Analyst
Biomedical Engineering, ML for healthcare focus

Ednah Mugoh (Kenya)
Applications/DevOps Developer
MSc Student, ML/Software Engineer, healthcare domain

John Williams Muga (Kenya)
Computer Vision Developer
Computer Science graduate, software developer

Yusupha Ceesay (The Gambia)
Applications/DevOps Developer
Clinical Trial Data Manager

🌍 Geographic Diversity Advantage

Our multi-national team brings diverse perspectives and understanding of different African healthcare contexts, ensuring our solution addresses varied regional needs and challenges.

Execution Plan & Timeline

Phase 1
Problem Identification & Domain Expert Consultation

Phase 2
Extensive Literature Review & State-of-Art Analysis

Phase 3
Data Collection & Dataset Preparation

Phase 4
Model Development & Training

Phase 5
Clinical Validation & Testing

Phase 6
Cross-Platform Application Development

Phase 7
Deployment & Real-World Testing

🎯 Key Deliverables

AI Model: Trained computer vision model for Pap smear analysis
Mobile Application: Offline-capable screening tool for rural clinics
Web Platform: Comprehensive diagnostic dashboard for medical professionals
Clinical Validation: Evidence-based accuracy and reliability metrics
Documentation: Implementation guides and training materials

🚀 Success Metrics

Technical: >90% accuracy in early-stage detection | Social: More streamlined and fast screening