Model Card for Brain Tumor Detection Model (YOLOv8)

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 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
Shared by: TekTonic AI
Model type: YOLOv8n image classifier
Language(s) (NLP): Not applicable
License: MIT
Finetuned from model: YOLOv8n pretrained backbone (Ultralytics)

Model Sources

Repository: [Add GitHub/Kaggle link here]
Paper [optional]: N/A
Demo [optional]: [Add Gradio/Streamlit link]

Uses

Direct Use

Used to classify brain MRI slices into one of the following categories:

Glioma Tumor
Meningioma Tumor
Pituitary Tumor
No Tumor

Downstream Use

Radiology assistant tools
Academic medical research
Mobile or embedded AI diagnostics

Out-of-Scope Use

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

Dataset sourced primarily from a single research collection
Class imbalance may exist
Model may be sensitive to image noise and artifacts

Recommendations

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

from ultralytics import YOLO
import cv2

model = YOLO("nzr_brain_tumor.pt")

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:

@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

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: [email protected]