ML Model Taxonomy for Medical Imaging

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ML Model Taxonomy for Medical Imaging


๐Ÿง  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?

Key Insight: Task-specific performance dominates over universal superiority. ResNet-50 wins on chest X-ray (98.37%), DeiT-Small dominates brain tumors (92.16%), while hybrid CNN+ViT models achieve 98.3% accuracy by combining local feature extraction with global context.

1. The Architecture Landscape: Three Paradigms

Medical imaging ML divides into three primary architectural families:

๐Ÿ”ฒ Convolutional Neural Networks (CNNs)

Era: Since 2012 (AlexNet)

Mechanism: Local feature extraction via convolution + pooling

Strengths: Fast, efficient, proven

Weaknesses: Limited global context

Examples: ResNet, DenseNet, EfficientNet

โญ Vision Transformers (ViTs)

Era: Since 2020 (Dosovitski)

Mechanism: Patch-based self-attention

Strengths: Global context, explainability

Weaknesses: Requires large data, higher compute

Examples: DeiT, Swin, Vision Transformer

๐Ÿ”— Hybrid Models

Era: Since 2022

Mechanism: CNN + ViT fusion

Strengths: Best of both, state-of-art

Weaknesses: Complex, harder to train

Examples: EViT-DenseNet, CvT, CoaT

2. Detailed Architecture Comparison

2.1 Convolutional Neural Networks (CNNs)

How They Work:

Input Image โ†’ Conv Layer (Extract local features) โ†’ Activation (ReLU)
  โ†“
Pooling (Reduce dimensions) โ†’ Conv + Activation โ†’ Pooling
  โ†“
Repeat 10-200+ layers
  โ†“
Fully Connected Layers โ†’ Output (Classification/Detection)

Key equation: I * K = ฮฃ I(x+i, y+j) ยท K(i,j)
(Convolution = element-wise multiplication + sum)

Strengths:

  • Efficiency: Local feature extraction = lower memory footprint
  • Speed: Optimized implementations widely available (CUDA, TensorRT)
  • Proven Track Record: 10+ years of medical applications
  • Small Data Performance: Work well with 1000-10000 training samples
  • Hardware Compatibility: Deployable on edge devices, mobile

Weaknesses:

  • Fixed Receptive Field: Kernel size determines what patterns it sees
  • Global Context Loss: Pooling layers discard spatial information
  • Black Box Problem: Grad-CAM helps but still opaque
  • Domain Shift Sensitivity: Performance drops when test data differs from training
  • Location Sensitivity: Can’t generalize lesion position well

Popular CNN Architectures for Medical Imaging:

Model Year Key Feature Best For
ResNet-50 2015 Residual connections (skip) Chest X-ray: 98.37%
DenseNet-169 2016 Dense connections Breast imaging, skin lesions
EfficientNet-B5 2019 Compound scaling Resource-constrained deployment
Inception-v4 2016 Multi-scale convolutions Polyp detection, lesions

2.2 Vision Transformers (ViTs)

How They Work:

Input Image (Hร—Wร—C) โ†’ Divide into N patches (Pร—P)
  โ†“
Linear Projection โ†’ Patch Embeddings (N ร— D)
  โ†“
Add Position Embeddings + [CLS] token
  โ†“
Transformer Encoder (Self-Attention blocks)
  โ”œโ”€ Query, Key, Value projections
  โ”œโ”€ Multi-head attention (8-16 heads)
  โ””โ”€ Feed-forward networks
  โ†“
Classification Head โ†’ Output

Key: Attention(Q,K,V) = softmax(QK^T / โˆšd_k) V
(Each patch attends to all other patches)

Strengths:

  • Global Context: Self-attention sees entire image at once
  • Long-Range Dependencies: Can model distant relationships (e.g., tumor + lymph node)
  • Interpretability: Attention maps show what model focuses on
  • Scalability: Performance improves with larger datasets
  • Task Flexibility: Same architecture works for classification, detection, segmentation

Weaknesses:

  • Data Hungry: Typically need 100K+ samples; medical data is limited
  • Computational Cost: Quadratic complexity O(nยฒ) in sequence length
  • Pre-training Required: Transfer learning from ImageNet essential
  • Patch Size Sensitivity: Fixed patch size (16ร—16) may miss fine details
  • Inference Latency: Slower than CNNs at deployment

Popular ViT Architectures for Medical Imaging:

Model Year Key Feature Medical Benchmark
Vision Transformer (ViT-B) 2020 Pure transformer ImageNet pre-trained
DeiT 2020 Distillation, small data Brain tumor: 92.16%
Swin Transformer 2021 Shifted windows Lung segmentation: 94.2%
CoaT 2021 CoAtNet hybrid Multi-modal fusion

2.3 Hybrid Models (CNN + ViT Fusion)

Why Hybrid?

Problem: CNNs miss global patterns, ViTs are data-hungry.

Solution: Let CNN extract local features, pass to ViT for global reasoning.

Architecture Pattern:

Input โ†’ CNN Stage (ResNet-50 backbone)
  โ”œโ”€ Conv1: Extract low-level edges (stride 2)
  โ”œโ”€ ResBlock1-4: Hierarchical features
  โ””โ”€ Output: Feature maps (H/32, W/32, 2048)
    โ†“
Reshape to patches โ†’ Transformer Encoder
  โ”œโ”€ Self-attention over patches
  โ”œโ”€ Fuse with CNN features
  โ””โ”€ Classification head
    โ†“
Output with explainability (attention + grad-cam)

Strengths:

  • Best Performance: 98.3% accuracy (vs 98% for pure approaches)
  • Data Efficiency: CNN backbone learns from smaller datasets
  • Dual Explainability: Both attention maps + feature visualizations
  • Modality Flexibility: Works across X-ray, CT, MRI, ultrasound
  • Inference Speed: Faster than pure ViT, more global than pure CNN

Popular Hybrid Models:

Model Fusion Strategy Best Performance
EViT-DenseNet169 DenseNet โ†’ ViT patches Skin cancer: 94.4%
CNN + SVM hybrid CNN features โ†’ ViT โ†’ SVM classifier Tumor detection: 98.3%
CvT (Convolutional Token) Conv tokenization + Transformer Medical segmentation: 96.1%

3. Task-Specific Performance: The Ground Truth

๐Ÿ“Š Real-World Benchmark Results (2024-2025)

Task Best Model Accuracy Architecture
Chest X-ray classification ResNet-50 98.37% CNN
Brain MRI tumor detection DeiT-Small 92.16% ViT
Lung disease detection Swin Transformer 94.2% ViT
Skin lesion classification EViT-DenseNet169 94.4% Hybrid
Tumor classification (general) ViT + EfficientNet 98.0% Hybrid
Tumor + SVM (multi-class) CNN + ViT + SVM 98.3% Hybrid
Brain tumor MRI ViT and EfficientNet (tied) 98.0% Both

4. Modality-Specific Recommendations

Decision Tree: Which Model to Use?

START: Choose Medical Imaging Model
โ”‚
โ”œโ”€ Modality?
โ”‚  โ”œโ”€ CHEST X-RAY
โ”‚  โ”‚  โ”œโ”€ Available data: <10K images?  โ†’ ResNet-50 (CNN) โœ“
โ”‚  โ”‚  โ”œโ”€ Available data: >50K images? โ†’ ResNet-50 + ViT hybrid
โ”‚  โ”‚  โ””โ”€ Need explainability? โ†’ GradCAM on ResNet โœ“
โ”‚  โ”‚
โ”‚  โ”œโ”€ BRAIN MRI (Tumor detection)
โ”‚  โ”‚  โ”œโ”€ Small hospital? โ†’ DeiT-Small (ViT, fast)
โ”‚  โ”‚  โ”œโ”€ Research setting? โ†’ Swin Transformer
โ”‚  โ”‚  โ””โ”€ Need real-time? โ†’ Lightweight hybrid (MobileNet + Attention)
โ”‚  โ”‚
โ”‚  โ”œโ”€ CT SCANS
โ”‚  โ”‚  โ”œโ”€ 2D slices? โ†’ EfficientNet-B5
โ”‚  โ”‚  โ”œโ”€ 3D volume? โ†’ 3D CNN (C3D, MedNet)
โ”‚  โ”‚  โ””โ”€ Multi-organ? โ†’ Swin Transformer 3D
โ”‚  โ”‚
โ”‚  โ”œโ”€ SKIN LESIONS
โ”‚  โ”‚  โ”œโ”€ <5K images? โ†’ DenseNet-121
โ”‚  โ”‚  โ”œโ”€ 10-100K images? โ†’ EViT-DenseNet169 (hybrid)
โ”‚  โ”‚  โ””โ”€ Open-source preferred? โ†’ DenseNet
โ”‚  โ”‚
โ”‚  โ”œโ”€ ULTRASOUND
โ”‚  โ”‚  โ”œโ”€ High noise? โ†’ ResNet-50 + denoising
โ”‚  โ”‚  โ”œโ”€ Limited labeled data? โ†’ DenseNet-161
โ”‚  โ”‚  โ””โ”€ Real-time diagnosis? โ†’ MobileNet
โ”‚  โ”‚
โ”‚  โ””โ”€ MULTIPLE MODALITIES
โ”‚     โ”œโ”€ Multi-modal fusion? โ†’ Transformer + attention
โ”‚     โ”œโ”€ Sequential analysis? โ†’ LSTM + CNN hybrid
โ”‚     โ””โ”€ Cross-modal learning? โ†’ Vision-Language Transformer
โ”‚
โ””โ”€ DEPLOYMENT CONSTRAINT?
   โ”œโ”€ Edge device (mobile)? โ†’ MobileNet, SqueezeNet
   โ”œโ”€ GPU available? โ†’ ResNet, Swin, ViT
   โ”œโ”€ Minimal latency? โ†’ ResNet-50 (CNN)
   โ””โ”€ Explainability critical? โ†’ Swin or hybrid + attention

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 10x more medical data to match CNN performance.

Implication for ScanLab: Always use transfer learning from pre-trained models. Never train ViT from scratch on medical data alone.

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 committing to production.

Finding #3: Domain Shift is the Real Enemy

Source: PMC11393140

Models trained on public datasets (Kaggle, ImageNet) 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

Source: Multiple 2024-2025 studies

When optimized, CNN+ViT hybrids achieve 0.3-2% higher accuracy than pure approaches. But require 40% more training time.

Trade-off: Higher accuracy vs. longer development cycle and complexity.

6. Data Requirements by Architecture

Architecture Minimum Data Optimal Data Training Time (GPU) Memory Usage
ResNet-50 1,000 10,000+ 2-6 hours 4GB
DenseNet-169 2,000 15,000+ 4-8 hours 6GB
EfficientNet-B5 3,000 20,000+ 6-12 hours 8GB
ViT-Base (pre-trained) 5,000 50,000+ 4-10 hours 8GB
Swin-Base (pre-trained) 5,000 100,000+ 8-16 hours 12GB
Hybrid (CNN+ViT) 3,000 30,000+ 8-20 hours 10GB

7. Explainability Comparison

How Each Architecture Explains Decisions:

  • CNN: Grad-CAM, activation maps (good but noisy)
  • ViT: Attention maps (clean, interpretable patches)
  • Hybrid: Dual explainability (CNN features + ViT attention)

Winner for Clinical Trust: ViT and Hybrid models. Physicians find attention maps more intuitive than gradient visualizations.

8. Recommendations for ScanLab

Phase 1: Initial Deployment

  • Start with: ResNet-50 for X-ray, DenseNet-169 for other modalities
  • Why: Proven, fast, require <10K training images
  • Explainability: Add Grad-CAM visualization

Phase 2: Scale (6 months)

  • Add: Swin Transformer for complex cases (CT, 3D volumes)
  • Strategy: Ensemble ResNet + Swin for higher confidence
  • Data: Collect Ukrainian-specific data for fine-tuning

Phase 3: Optimize (12 months)

  • Develop: Custom hybrid model (DenseNet backbone + ViT encoder)
  • Target: 98%+ accuracy with clinician-friendly explanations
  • Validation: Compare to radiologist performance in ScanLab trials

9. References

  1. PMC11393140 โ€” "Comparison of Vision Transformers and CNNs in Medical Image Analysis: Systematic Review" (2024)
  2. PMC12701147 โ€” "Vision Transformers in Medical Imaging: Comprehensive Review Across Multiple Diseases" (2025)
  3. ArXiv 2507.21156v1 โ€” "Comparative Analysis of Vision Transformers and CNNs for Medical Image Classification" (2025)
  4. AICompetence.org โ€” "Vision Transformers Vs CNNs: Who Leads Vision In 2025?" (2025)
  5. R001-R002 (from MEMORY) โ€” Recent advances in medical image classification

Questions Answered

โœ… How do CNN, ViT, and hybrid models compare?
CNNs fast & efficient, ViTs excel at global context, hybrids achieve best accuracy (98.3%) by combining both.

โœ… Which architecture is best for specific modalities?
X-ray โ†’ ResNet-50; Brain MRI โ†’ DeiT/Swin; General โ†’ EViT-DenseNet hybrid; Complex 3D โ†’ Swin 3D.

Open Questions for Future Articles

  • How do we handle data imbalance (rare diseases) in each architecture?
  • Can federated learning training work with hybrid models?
  • What's the impact of multi-modal input (MRI + CT + reports) on architecture choice?

Next Article: "Data Requirements and Quality Standards" โ€” exploring minimum dataset sizes, labeling protocols, and augmentation strategies.

Stabilarity Hub Research Team | hub.stabilarity.com