sowmya333/thyroid-malignancy-detector-convnext

Thyroid Nodule Malignancy Detector (5-Fold Validated)

This model uses a Dual-Stream Spatial-Frequency Fusion architecture (ConvNeXt-Tiny + FFT Magnitude Spectrum) to classify thyroid nodules in ultrasound images.

📊 5-Fold Cross-Validation Performance

The model was evaluated using a stratified 5-fold cross-validation on the consolidated dataset.

Fold	Accuracy	AUC	Sensitivity	Specificity
0	0.881728	0.94519	0.92364	0.79386
1	0.866856	0.939398	0.878661	0.842105
2	0.869688	0.945065	0.899582	0.807018
3	0.871013	0.934528	0.900628	0.808791
4	0.858965	0.930304	0.875523	0.824176

Summary Statistics:

• Mean AUC: 0.9389 ± 0.0065
• Mean Sensitivity: 0.8956 ± 0.0195
• Mean Specificity: 0.8152 ± 0.0185

🚀 Clinical Application

The weights hosted here (pytorch_model.bin) correspond to Fold 0, which achieved the highest individual AUC of 0.9452.

🛠 Methodology

• Backbone: ConvNeXt-Tiny (Spatial Stream)
• Texture Analysis: FFT Magnitude Spectrum (Frequency Stream)
• Preprocessing: CLAHE
• Loss Function: Focal Loss (α=1, γ=2)