Algorithm Workflow
Our multi-stage deep learning pipeline for leukemia cell classification
Methodology & Approach
Detailed breakdown of our multi-stage deep learning approach
Stage 1: Cell Segmentation
Attention U-Net for precise cell boundary detection
Multi-scale attention mechanisms for improved segmentation accuracy
Post-processing for noise reduction and boundary refinement
Stage 2: Feature Extraction (HybridCN-NAS)
ConvNeXt Large backbone for robust feature representation
NASNet Large integration for architecture optimization
Multi-scale feature fusion from both networks
Deep feature extraction capturing cellular morphology
Stage 3: Feature Normalization & Selection
Z-score normalization for feature standardization
mRMR (minimum Redundancy Maximum Relevance) feature selection
Selection of top discriminative features for classification
Dimensionality reduction to enhance model performance
Stage 4: Classification & Visualization
Multi-Head Self-Attention classifier for final prediction
Grad-CAM visualization for explainable predictions
Probabilistic output with confidence estimation
Four-class classification: Benign, Early, Pre, Pro
Key Innovations
Novel contributions and technological advances in our approach
HybridCN-NAS architecture combining ConvNeXt and NASNet
Attention U-Net for precise cell segmentation
Multi-head self-attention for robust classification
Grad-CAM for explainable AI and clinical interpretability and feature selection using mRMR
Technical Architecture
Deep learning components and model architecture details
Cell Segmentation
Attention U-Net
Advanced segmentation network with attention mechanisms for precise cell boundary detection
Multi-scale Processing
Hierarchical feature extraction at multiple scales for comprehensive cell analysis
Feature Extraction
ConvNeXt Large
Modern CNN architecture with improved efficiency and performance
NASNet Large
Neural Architecture Search optimized network for automated feature learning
Hybrid Fusion
Intelligent combination of features from both architectures
Feature Selection
Z-score Normalization
Statistical normalization for feature standardization and improved convergence
mRMR Selection
Advanced feature selection based on relevance and redundancy criteria
Classification
Multi-Head Self-Attention
Transformer-inspired attention mechanism for robust classification
Grad-CAM Visualization
Explainable AI technique for visualizing important regions in classification
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