🔬 Research Overview

This research challenges a fundamental assumption in medical image analysis: that complex, multi-stage preprocessing pipelines universally improve deep learning model performance. Through a systematic ablation study on the HAM10000 dataset, we demonstrate that preprocessing effectiveness is strongly architecture-dependent, with CNNs and Vision Transformers requiring fundamentally different optimization strategies.

🎯 Key Findings

• Architecture Dependence: ResNet50 achieves optimal performance (85.07%) with simple noise reduction, while ViT requires ground truth segmentation (83.03%) for best results
• Individual > Combined: Single preprocessing techniques consistently outperform complex multi-stage pipelines
• Clinical Significance: Up to 8.09% accuracy improvement with proper architecture-specific preprocessing selection
• Resource Efficiency: Simple, targeted preprocessing provides better cost-benefit ratios than comprehensive pipelines
• Statistical Rigor: Large effect sizes (Cohen's d: 0.82-2.01) demonstrate practical significance despite limited statistical power
• Deployment Guidelines: Evidence-based recommendations for resource-constrained vs. high-accuracy clinical scenarios

🛠️ Methodology & Technical Stack

Dataset & Experimental Design

• HAM10000 Dataset: 2,013 dermatoscopic images (binary classification: benign vs. malignant)
• 7 Preprocessing Configurations: Systematic evaluation from baseline (C1) to full pipeline (C7)
• 2-Fold Stratified Cross-Validation: Rigorous evaluation protocol ensuring balanced class distribution
• Statistical Analysis: Paired t-tests, Bonferroni correction, Cohen's d effect size calculation

Deep Learning Architectures

• ResNet50: 25.6M parameters, hierarchical CNN feature extraction
• Vision Transformer (ViT-Base): 86.6M parameters, global attention mechanism, 16×16 patch size
• Training Protocol: Batch size 16, LR 1e-6, Adam optimizer, 5 epochs
• Hardware: NVIDIA GPU with CUDA support, 16GB minimum GPU memory

Preprocessing Techniques

• Hair Removal (C2): Morphological black-hat filtering + OpenCV inpainting
• Noise Reduction (C3): Sequential median (5×5) + Gaussian filtering (σ=1.0)
• Ground Truth Segmentation (C4): Binary mask application for lesion isolation
• Combined Approaches (C5-C7): Systematic evaluation of technique interactions

Software Implementation

• PyTorch: Deep learning framework for model training and evaluation
• OpenCV: Image preprocessing and morphological operations
• Scikit-learn: Statistical analysis and cross-validation
• NumPy/Pandas: Data manipulation and numerical computing
• Matplotlib/Seaborn: Result visualization and publication-quality figures

📊 Experimental Framework

The study implements a systematic seven-phase methodology:

1. Dataset Preparation: HAM10000 filtering to 2,013 balanced samples for binary classification
2. Architecture Selection: Preliminary benchmarking of 4 SOTA models (ViT, Swin, ResNet50, EfficientNet)
3. Configuration Design: 7 preprocessing combinations testing individual vs. combined techniques
4. Experimental Execution: 2 architectures × 7 configurations × 2 folds = 28 training runs
5. Statistical Analysis: Comprehensive hypothesis testing with multiple comparison correction
6. Results Validation: Cross-validation consistency checks and preprocessing integrity verification
7. Clinical Translation: Deployment guidelines and resource allocation recommendations

🚀 Clinical Impact & Applications

Immediate Clinical Applications

• Resource-Constrained Environments: Deploy ResNet50 with simple noise reduction for 85.07% accuracy with minimal computational overhead
• High-Accuracy Scenarios: Use ViT with segmentation for 83.03% accuracy when segmentation masks are available
• Telemedicine Systems: Architecture-specific optimization enables efficient remote dermatology screening
• Point-of-Care Diagnostics: Simplified preprocessing pipelines facilitate mobile device deployment

Research Implications

• Challenges Universal Preprocessing Assumptions: Demonstrates architecture-dependent optimization requirements
• Resource Allocation Guidance: Evidence for prioritizing model optimization over preprocessing complexity
• Cost-Benefit Analysis: Simple techniques provide better performance per computational dollar
• Future Research Direction: Integration of preprocessing optimization into neural architecture search

📈 Research Results Summary

ResNet50 Performance

Vision Transformer Performance

Statistical Significance

• Cohen's d Effect Sizes: 0.82-2.01 (medium to large practical significance)
• p-values: 0.089-0.345 (limited statistical significance due to sample size)
• Bonferroni Corrected α: 0.00238 (21 pairwise comparisons per architecture)
• Clinical Significance: All major improvements exceed 3% MCID threshold

📚 Publications & Documentation

Research Paper

• Full Paper: Available in the GitHub repository
• Methodology Flowchart: See documentation in the repo
• Presentation Materials: Complete presentation materials available in the repository

Target Journals

• Primary: Medical Image Analysis (Elsevier) - IF: 8.880
• Secondary: IEEE Transactions on Medical Imaging - IF: 10.6
• Alternative: Computer Methods and Programs in Biomedicine - IF: 6.1

🤝 Collaboration & Contributions

This research is open for:

• Multi-dataset validation studies across ISIC, BCN20000, and clinical datasets
• Extended architecture comparisons with EfficientNet, Swin Transformers, modern architectures
• Real-world clinical deployment pilot studies in hospital settings
• Preprocessing technique expansion to color normalization, illumination correction

Contact

• Repository: GitHub - skinCheckNotebooks
• Branch: paper (contains complete research materials)
• Issues: Open for feature requests, methodology discussions, and collaboration proposals

📝 Acknowledgments

• Dataset: HAM10000 (Tschandl et al., 2018) - publicly available dermatoscopic image collection
• Frameworks: PyTorch, OpenCV, Scikit-learn open-source communities
• Inspiration: Challenges to conventional preprocessing assumptions in medical imaging literature

🎓 Citation

If you use this research or methodology in your work, please cite:

@article{architecture_specific_preprocessing_2025,
  title={Architecture-Specific Preprocessing Optimization for Automated Skin Lesion Classification: A Comprehensive Ablation Study},
  author={[Author Names]},
  journal={[Target Journal]},
  year={2025},
  note={Demonstrates architecture-dependent preprocessing effectiveness in medical image classification}
}

Last Updated: November 20, 2025
Status: Research Complete - Preparing for Journal Submission
Expected Publication: Q2 2026