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README.md

@@ -9,34 +9,34 @@ tags:
 - convnext
 - explainable-ai
 metrics:
-- auc: 0.9362
-- accuracy: 0.8681
+- auc: 0.9414
+- accuracy: 0.8747
 ---
 # 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 7,058 image dataset.
+The model was evaluated using a stratified 5-fold cross-validation on the consolidated dataset.
 
 |   Fold |   Accuracy |      AUC |   Sensitivity |   Specificity |
 |-------:|-----------:|---------:|--------------:|--------------:|
-|      1 |   0.884561 | 0.94365  |      0.912134 |      0.826754 |
-|      2 |   0.86898  | 0.936338 |      0.881799 |      0.842105 |
-|      3 |   0.875354 | 0.944329 |      0.912134 |      0.798246 |
-|      4 |   0.860383 | 0.932138 |      0.876569 |      0.826374 |
-|      5 |   0.851169 | 0.924778 |      0.872385 |      0.806593 |
+|      0 |   0.883144 | 0.9435   |      0.92364  |      0.798246 |
+|      1 |   0.876062 | 0.941074 |      0.898536 |      0.828947 |
+|      2 |   0.883853 | 0.943223 |      0.927824 |      0.791667 |
+|      3 |   0.866761 | 0.938833 |      0.906904 |      0.782418 |
+|      4 |   0.863926 | 0.940572 |      0.881799 |      0.826374 |
 
 ### Summary Statistics:
-- **Mean AUC:** 0.9362 ± 0.0082
-- **Mean Sensitivity:** 0.8910 ± 0.0196
-- **Mean Specificity:** 0.8200 ± 0.0175
+- **Mean AUC:** 0.9414 ± 0.0019
+- **Mean Sensitivity:** 0.9077 ± 0.0188
+- **Mean Specificity:** 0.8055 ± 0.0210
 
 ## 🚀 Clinical Application
-This model is designed for clinical decision support. The weights hosted here (pytorch_model.bin) correspond to **Fold 3**, which achieved the highest individual AUC of 0.9443.
+The weights hosted here (pytorch_model.bin) correspond to **Fold 0**, which achieved the highest individual AUC of 0.9435.
 
 ## 🛠 Methodology
 - **Backbone:** ConvNeXt-Tiny (Spatial Stream)
 - **Texture Analysis:** FFT Magnitude Spectrum (Frequency Stream)
-- **Preprocessing:** CLAHE (Contrast Limited Adaptive Histogram Equalization)
-- **Loss Function:** Focal Loss (α=1, γ=2) to handle class imbalance.
+- **Preprocessing:** CLAHE
+- **Loss Function:** Focal Loss (α=1, γ=2)