Algorithm Workflow
Our multi-stage deep learning pipeline for ovarian cancer detection
Methodology & Approach
Detailed breakdown of our multi-stage deep learning approach
Stage 1: Image Preprocessing
CLAHE (Contrast Limited Adaptive Histogram Equalization) for contrast enhancement
Bilateral filtering for noise reduction while preserving edges
Standardized resizing and normalization for consistent input
Stage 2: Feature Extraction
Pre-trained ResNet-50 backbone with attention mechanisms
Custom attention blocks for focusing on relevant image regions
Deep feature extraction capturing both low-level and high-level patterns
Stage 3: Feature Selection
SHAP (SHapley Additive exPlanations) values for interpretable feature selection
Selection of top 500 features based on importance ranking
Dimensionality reduction to enhance model performance
Stage 4: Classification
Attention-based CNN classifier for final prediction
Probabilistic output with confidence estimation
Robust performance across diverse imaging conditions
Key Innovations
Novel contributions and technological advances in our approach
Attention mechanisms for interpretable predictions
Multi-stage feature selection for optimal performance
Comprehensive preprocessing pipeline for robust results
Explainable AI techniques for clinical trust
Technical Architecture
Deep learning components and model architecture details
Feature Extraction
AttentionResNet50
Pre-trained ResNet-50 with custom attention mechanisms for enhanced feature learning
Attention Blocks
Focus on relevant image regions while suppressing noise and artifacts
Feature Selection
SHAP Analysis
Explainable AI for interpretable feature importance ranking
Select 500 Features
Dimensionality reduction while retaining critical information
Classification
AttCNN Classifier
Attention-based CNN for final cancer/non-cancer prediction
Confidence Estimation
Probabilistic output with uncertainty quantification
Model Performance Metrics
Experience Our Algorithm in Action
Test our multi-stage deep learning approach with your own medical images or explore our sample dataset