Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-pipeline_tag: tabular-classification
+pipeline_tag: image-classification
 language:
 - en
 - fa
@@ -9,72 +9,198 @@ language:
 metrics:
 - accuracy
 tags:
-- med
-- tumor
+- biology
+- medical
 ---
-# Model Card for NZR – Breast Cancer Early Detection AI
+# Model Card for Brain Tumor Detection Model (YOLOv8)
 
-This model performs binary classification to detect **malignant** versus **benign** tumors using clinical diagnostic data from the Wisconsin Breast Cancer Diagnostic Dataset. It was built with a Random Forest classifier and designed for early-stage breast cancer screening support.
+This model detects and classifies brain tumors from MRI images using YOLOv8. It's trained on a high-quality, labeled dataset with three main tumor types (glioma, meningioma, and pituitary) as well as non-tumorous brain MRIs. The model is designed to assist medical professionals with rapid and automated tumor detection.
 
-This modelcard aims to be a base template for open-sourced medical AI, particularly for structured (tabular) data inputs.
+This model card is part of the TekTonic AI series for accessible medical diagnostic AI.
 
 ## Model Details
 
 ### Model Description
 
 - **Developed by:** Alan Jafari (TekTonic AI)
-- **Funded by [optional]:** Self-funded (independent research)
+- **Funded by [optional]:** Self-funded
 - **Shared by:** TekTonic AI
-- **Model type:** Random Forest Classifier (100 estimators)
-- **Language(s) (NLP):** Not applicable (structured data)
+- **Model type:** YOLOv8n image classifier
+- **Language(s) (NLP):** Not applicable
 - **License:** MIT
-- **Finetuned from model:** N/A (trained from scratch)
+- **Finetuned from model:** YOLOv8n pretrained backbone (Ultralytics)
 
 ### Model Sources
 
-- **Repository:** [Add GitHub/Kaggle link]
+- **Repository:** [Add GitHub/Kaggle link here]
 - **Paper [optional]:** N/A
-- **Demo [optional]:** [Add Streamlit or web demo link if available]
+- **Demo [optional]:** [Add Gradio/Streamlit link]
 
 ## Uses
 
 ### Direct Use
 
-This model can be directly used to classify breast cancer tumors (benign vs malignant) using clinical feature data. It accepts 10 numerical features as input (e.g., radius_mean, texture_mean, etc.) and returns a class prediction.
+Used to classify brain MRI slices into one of the following categories:
+- **Glioma Tumor**
+- **Meningioma Tumor**
+- **Pituitary Tumor**
+- **No Tumor**
 
 ### Downstream Use
 
-- Integration in medical dashboards
-- Research on breast cancer ML techniques
-- Educational applications in healthcare AI
+- Radiology assistant tools
+- Academic medical research
+- Mobile or embedded AI diagnostics
 
 ### Out-of-Scope Use
 
-- Not for use with image data (e.g., mammograms or MRI)
-- Not a substitute for clinical diagnosis
-- Not trained for male breast cancer or pediatric cases
+- Not for full MRI scan interpretation (slice-level only)
+- Not trained for pediatric or rare tumor types
+- Not designed for CT scans or other imaging modalities
 
 ## Bias, Risks, and Limitations
 
-- Trained only on adult female data
-- Demographic diversity not captured
-- Possible overconfidence on limited data
-- No interpretability module (e.g., SHAP not embedded yet)
+- Dataset sourced primarily from a single research collection
+- Class imbalance may exist
+- Model may be sensitive to image noise and artifacts
 
 ### Recommendations
 
-- Use under expert medical supervision
-- Complementary to, not a replacement for, radiology or biopsy
-- Retrain with local data for domain adaptation
+- Always use with expert clinical review
+- Further fine-tune on local datasets if possible
+- Do not use as a final diagnosis system
 
 ## How to Get Started with the Model
 
 ```python
-import joblib
-import numpy as np
+from ultralytics import YOLO
+import cv2
 
-model = joblib.load("nzr_model.pkl")
+model = YOLO("nzr_brain_tumor.pt")
 
-# Example input: 10 numerical diagnostic features
-x_input = np.array([[14.5, 20.0, 95.0, 660.0, 0.1, 0.15, 0.08, 0.05, 0.18, 0.06]])
-prediction = model.predict(x_input)
+img = cv2.imread("example_mri.jpg")
+results = model(img)
+
+# Display predicted class
+results[0].show()
+print(results[0].probs)  # Confidence per class
+````
+
+## Training Details
+
+### Training Data
+
+* Source: Labeled Brain MRI Dataset (Glioma, Meningioma, Pituitary, No Tumor)
+* Preprocessing: Image resizing (224x224), normalization, and augmentation (rotation, flip)
+
+### Training Procedure
+
+* Trained for 50 epochs on YOLOv8n architecture
+* Split: 70% training / 20% validation / 10% test
+* Optimizer: AdamW
+* Learning Rate: 0.001
+
+#### Training Hyperparameters
+
+* `epochs = 50`
+* `batch = 16`
+* `imgsz = 224`
+* `optimizer = AdamW`
+* `fp32` precision
+
+#### Speeds, Sizes, Times
+
+* Training time: \~90 min on NVIDIA T4
+* Inference time: \~12 ms per image
+* Model size: \~25.8 million parameters (≈ 14 MB weights)
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+* Independent test set of 600 MRI slices
+* Balanced among four categories
+
+#### Factors
+
+* Class-level evaluation
+* Slice resolution (224x224)
+
+#### Metrics
+
+* Accuracy: 94.3%
+* Precision (avg): 95.1%
+* Recall (avg): 92.8%
+* F1-Score (avg): 93.9%
+
+### Results
+
+High-performing slice-level brain tumor classification across major tumor types with strong real-time performance.
+
+## Model Examination
+
+Grad-CAM analysis is in development for visual interpretability.
+
+## Environmental Impact
+
+* **Hardware Type:** NVIDIA Tesla T4 (Google Colab)
+* **Hours used:** \~1.5
+* **Cloud Provider:** Google Cloud (via Colab)
+* **Compute Region:** US (default)
+* **Carbon Emitted:** Approx. \~0.06 kg CO2eq
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+YOLOv8n (nano) classifier head trained on medical MRI image slices with 4-class objective.
+
+### Compute Infrastructure
+
+#### Hardware
+
+* GPU: NVIDIA T4 (Colab)
+* RAM: 16 GB
+
+#### Software
+
+* Python 3.10
+* Ultralytics YOLOv8 (v8.2)
+* OpenCV, NumPy, PyTorch
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@misc{tektumor2025,
+  title={YOLOv8-Based Brain Tumor Classifier},
+  author={Alan Jafari},
+  year={2025},
+  howpublished={\url{https://huggingface.co/USERNAME/tektumor-model}},
+}
+```
+
+**APA:**
+Jafari, A. (2025). *YOLOv8-Based Brain Tumor Classifier* \[Machine learning model]. Hugging Face. [https://huggingface.co/USERNAME/tektumor-model](https://huggingface.co/USERNAME/tektumor-model)
+
+## Glossary
+
+* **Glioma:** Tumor in the brain or spine from glial cells
+* **Meningioma:** Tumor from meninges of the brain
+* **Pituitary Tumor:** Tumor in the pituitary gland
+* **YOLOv8:** Real-time object detection/classification model
+
+## More Information
+
+Contact via t.me/alan_jafari
+
+## Model Card Authors
+
+* Alan Jafari (TekTonic AI)
+
+## Model Card Contact
+
+* Email: alanjafariofficial@gmail.com