Update README.md

README.md

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+# ACE-V1.1: Brain Tumor Detection
+
+**ACE-V1.1** is a specialized computer vision model fine-tuned for MRI brain tumor detection. This version is a critical update that eliminates "hallucinations" (False Positives) in healthy brain tissue.
+
+---
+### Hardware & Environment
+* **Training Platform:** MacBook Pro (M1 Pro Chip)
+* **Acceleration:** Apple Silicon Metal Performance Shaders (MPS)
+* **Framework:** Ultralytics YOLOv11
+* **Total Epochs:** ACE-V1 (90) + Finetuning ACE-V1.1 (30) = **120 Total Epochs**
+---
+
+### Key Improvements in V1.1
+* **False-Positive Rate:** Achieved **1.00 Specificity** on healthy brain scans.
+* **Accuracy:** Verified **0.925 mAP@0.5** on the independent test set.
+* **Performance:** Optimized for a high F1-score to ensure reliable clinical support.
+
 ---
-library_name: ultralytics
-license: cc-by-nc-4.0
-tags:
-- tumor-detection
-- brain-mri
-- yolov11
-- medical-imaging
-- brisc-2025
-- sota
-metrics:
-- mAP
-pipeline_tag: object-detection
+
+### Performance & Validation
+| Metric | Value |
+| :--- | :--- |
+| **mAP50** | **0.925** |
+| **Precision** | **91.1%** |
+| **Recall** | **89.7%** |
+| **Background Specificity** | **1.00 (Perfect)** |
+
+#### **Validation Proof**
+![Confusion Matrix](confusion_matrix_normalized.png)
+*Figure 1: Normalized Confusion Matrix showing perfect separation of healthy tissue (Background).*
+
+![Precision-Recall Curve](BoxPR_curve.png)
+*Figure 2: Precision-Recall curve confirming the 0.925 mAP score.*
+
+> **Note on Training Logs:** The `results.png` file reflects a high-intensity training run conducted without a validation split (`val=False`) to maximize the training data pool. Final metrics were verified using a separate hold-out test set as shown in the PR and F1 curves.
+
 ---
+### Operational Guide
+For the most reliable results, I recommend the following inference settings based on the F1-Confidence analysis:
+
+* **Recommended Confidence:** `0.466`
+* **Image Size:** `640x640`
 
-# ACE-V1: Brain Tumor Localization
-ACE-V1 is a YOLO11 model trained on the **BRISC 2025** dataset for localizing tumors in MRI scans.
+```python
+from ultralytics import YOLO
 
-### **Performance**
-* **mAP@50:** 0.92777 (Verified on test set)
-* **Optimal Weights:** Captured at **Epoch 65** (see `results.png` for convergence history).
-* **Target Class:** `tumor` (single-class detection).
+# Load the ACE-V1.1 weights
+model = YOLO('ACE-V1.1.pt')
 
-### **⚠️ Dataset Note: BRISC 2025 v1.0**
-I am aware of reports regarding "Data Leakage" in the v1.0 training/validation splits due to duplicate images.
-* **Current Status:** This model (ACE-V1) represents peak performance on the original dataset release.
-* **Future Updates:** I am monitoring the official BRISC 2025 repository. Once a "Clean v2.0" dataset is released, I will re-train and publish **ACE-V2** to ensure complete scientific integrity.
+# Run inference with the optimal threshold
+results = model.predict(source='mri_scan.jpg', conf=0.466, save=True)