ML Model Taxonomy for Medical Imaging

ML Model Taxonomy for Medical Imaging

Article #4 in “Machine Learning for Medical Diagnosis” Research Series
By Oleh Ivchenko, Researcher, ONPU | Stabilarity Hub | February 8, 2026
Questions Addressed: How do CNN, ViT, and hybrid models compare for medical imaging? Which architecture is best for specific modalities?

1. The Architecture Landscape: Three Paradigms #

Medical imaging ML divides into three primary architectural families. The following diagram illustrates their key characteristics and relationships:

graph TD
    A[Medical Imaging ML] --> B[CNNs
Since 2012]
    A --> C[Vision Transformers
Since 2020]
    A --> D[Hybrid Models
Since 2022]
    
    B --> B1[Local Feature Extraction]
    B --> B2[Fast & Efficient]
    B --> B3[ResNet, DenseNet, EfficientNet]
    
    C --> C1[Global Self-Attention]
    C --> C2[Patch-Based Processing]
    C --> C3[DeiT, Swin, ViT-B]
    
    D --> D1[CNN Backbone + ViT Encoder]
    D --> D2[Best of Both Paradigms]
    D --> D3[EViT-DenseNet, CvT, CoaT]
    
    style A fill:#000,color:#fff
    style B fill:#4CAF50,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#9C27B0,color:#fff

2.1 Convolutional Neural Networks (CNNs) #

Strengths: Efficiency, speed, proven track record (10+ years), small data performance (1K–10K samples), hardware compatibility for edge/mobile deployment.
Weaknesses: Fixed receptive field, global context loss through pooling, black-box opacity, domain shift sensitivity.

2.2 Vision Transformers (ViTs) #

Strengths: Global context via self-attention, long-range dependencies, interpretable attention maps, scalability with larger datasets, task flexibility across classification/detection/segmentation.
Weaknesses: Data hungry (100K+ samples), quadratic complexity O(n²), mandatory pre-training, patch size sensitivity, slower inference than CNNs.

2.3 Hybrid Models (CNN + ViT Fusion) #

Why Hybrid? CNNs miss global patterns; ViTs are data-hungry. Solution: CNN extracts local features, ViT handles global reasoning. When optimized, CNN+ViT hybrids achieve 0.3–2% higher accuracy than pure approaches, requiring ~40% more training time.

3. Task-Specific Performance Benchmarks #

xychart-beta
    title "Architecture Accuracy by Medical Imaging Task (%)"
    x-axis ["Chest X-ray", "Brain MRI", "Lung Disease", "Skin Lesion", "Tumor (Multi)", "Tumor+SVM"]
    y-axis "Accuracy %" 88 --> 100
    bar [98.37, 92.16, 94.2, 94.4, 98.0, 98.3]

4. Modality-Specific Decision Framework #

flowchart TD
    START([Choose Medical Imaging Model]) --> MOD{Imaging Modality?}
    
    MOD --> CXR[Chest X-Ray]
    MOD --> BMRI[Brain MRI]
    MOD --> CT[CT Scans]
    MOD --> SKIN[Skin Lesions]
    MOD --> US[Ultrasound]
    MOD --> MULTI[Multiple Modalities]
    
    CXR --> CXR1{Large dataset?}
    CXR1 -->|50K+| CXR2[ResNet-50 + ViT Hybrid]
    CXR1 -->Small| CXR3[ResNet-50 + GradCAM]
    
    BMRI --> BMRI1{Use case?}
    BMRI1 -->Small hospital| BMRI2[DeiT-Small]
    BMRI1 -->Research| BMRI3[Swin Transformer]
    BMRI1 -->Real-time| BMRI4[MobileNet + Attention]
    
    CT --> CT1{Dimensions?}
    CT1 -->|2D slices| CT2[EfficientNet-B5]
    CT1 -->|3D volume| CT3[3D CNN / MedNet]
    CT1 -->Multi-organ| CT4[Swin Transformer 3D]
    
    SKIN --> SKIN1{Dataset size?}
    SKIN1 -->Less than 5K| SKIN2[DenseNet-121]
    SKIN1 -->|10-100K| SKIN3[EViT-DenseNet169]
    
    US --> US1{Priority?}
    US1 -->High noise| US2[ResNet-50 + Denoising]
    US1 -->Limited labels| US3[DenseNet-161]
    US1 -->Real-time| US4[MobileNet]
    
    MULTI --> MULTI1[Transformer + Attention]
    
    style START fill:#000,color:#fff
    style CXR2 fill:#4CAF50,color:#fff
    style BMRI3 fill:#4CAF50,color:#fff
    style CT4 fill:#4CAF50,color:#fff
    style SKIN3 fill:#4CAF50,color:#fff

5. Critical Insights from Systematic Review #

Finding #1: Pre-training Matters for ViTs (Source: PMC11393140, 36-study systematic review)
ViT models perform 15–20% better when pre-trained on ImageNet. Without pre-training, they require 10× more medical data to match CNN performance. Implication: Always use transfer learning from pre-trained models.

Finding #2: Task-Specific Architecture Wins (Source: ArXiv 2507.21156v1, 2025)
No universal winner. Architecture choice matters more than model size. ResNet-50 beats DenseNet-201 on X-ray despite smaller depth. Implication: Benchmark all three paradigms on your specific dataset before production.

Finding #3: Domain Shift is the Real Enemy (Source: PMC11393140)
Models trained on public datasets drop 5–15% accuracy on real clinical data from different hospitals/equipment. Solution: Fine-tune on local data. ViTs handle this better than CNNs due to global context adaptation.

Finding #4: Hybrid Models Consistently Win on Benchmarks
CNN+ViT hybrids achieve 0.3–2% higher accuracy than pure approaches, but require 40% more training time.

6. Data Requirements by Architecture #

7. Recommendations for ScanLab Implementation #

Phase 1 (Initial Deployment): Start with ResNet-50 for X-ray, DenseNet-169 for other modalities. Proven, fast, require <10K training images. Add Grad-CAM visualization for explainability.

Phase 2 (6 months — Scale): Add Swin Transformer for complex cases (CT, 3D volumes). Use ensemble ResNet + Swin for higher confidence. Collect Ukrainian-specific data for fine-tuning.

Phase 3 (12 months — Optimize): Develop custom hybrid model (DenseNet backbone + ViT encoder). Target: 98%+ accuracy with clinician-friendly explanations. Validate against radiologist performance in ScanLab trials.