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Technical Architecture

Progressive Fine-Tuning Pipeline

CW3BO: Class Weight Balancing via Bayesian Optimization

Problem: Class imbalance causes bias toward majority classes (Normal: 5,077 images, Cyst: 3,709, Tumor: 2,283, Stone: 1,377).

Solution: Automated search for optimal class weights using Gaussian Process-based Bayesian optimization.

Test-Time Augmentation (TTA)

Generated multiple augmented versions of each test image to reduce prediction variance and improve generalization.

Performance Metrics

Overall Test Accuracy: 99.92% - Balanced performance across all classes demonstrates effective handling of dataset imbalance.

Comparison with State-of-the-Art

Key Innovations

• First Progressive Fine-Tuning for Medical Imaging: Gradual layer unfreezing prevents catastrophic forgetting and enables stable adaptation to kidney domain.
• Automated Class Weight Optimization: Bayesian approach eliminates manual trial-and-error, systematically finding optimal balance for imbalanced medical datasets.
• TTA for Minority Class Enhancement: Multiple augmented views particularly benefit under-represented tumor and stone classes, reducing bias.
• Computational Efficiency: DenseNet201's dense connectivity provides high accuracy (99.92%) with moderate parameters (20M vs 55.9M in InceptionResNetV2).

Clinical Impact

This system has potential to assist radiologists in:

• Early Detection: 99.92% accuracy enables reliable identification of kidney tumors, cysts, and stones in CT scans.
• Diagnostic Efficiency: Automated classification reduces manual review time, allowing physicians to focus on treatment planning.
• Reduced Misdiagnosis: Balanced performance across classes (99.8-100% per-class metrics) minimizes false negatives for critical tumor cases.
• Scalability: Can process large volumes of CT scans in screening programs or resource-limited settings.

Implementation Details