README.md

metadata

language: en
license: mit
tags:
  - deep-learning
  - medical-imaging
  - tumor-detection
  - MRI
  - h5
model-index:
  - name: MRI_LLM
    results:
      - task:
          type: image-classification
        dataset:
          name: Public MRI datasets (Kaggle, NIH, TCIA)
          type: image
        metrics:
          - type: accuracy
            value: 95.2

MRI_LLM: Brain, Breast, and Lung Tumor Detection Models

📌 Author: Vijayendher Gatla (@wizaye)
📌 Repository: https://huggingface.co/wizaye/MRI_LLM
📌 License: MIT
📌 Tags: deep-learning, medical-imaging, tumor-detection, MRI, h5

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Model Overview

The MRI_LLM repository contains three deep learning models trained for tumor detection in brain, breast, and lung MRIs. These models leverage deep neural networks to assist in the automated diagnosis of tumors from medical imaging data.

Models Included

• Brain Tumor Model (brain_model.h5): Detects tumors in MRI brain scans.
• Breast Tumor Model (breast_tumor.h5): Identifies malignant and benign breast tumors.
• Lung Tumor Model (lung_tumor.h5): Predicts lung tumors using CT/MRI scans.

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Intended Use

These models are designed for research and educational purposes. They can be used for:
✅ Assisting radiologists in medical image analysis
✅ Experimenting with deep learning in healthcare
✅ Further fine-tuning on custom datasets

⚠️ Disclaimer: These models are not FDA/CE-approved and should not be used for clinical diagnosis.

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Model Architecture

Each model is based on Convolutional Neural Networks (CNNs), specifically optimized for medical image classification. The architecture includes:

• Feature extraction layers for capturing patterns in MRI scans
• Fully connected layers for classification
• Softmax/Sigmoid activation depending on the number of classes

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Dataset

• The models were trained on publicly available MRI datasets (e.g., Kaggle, NIH, TCIA).
• Data preprocessing included normalization, augmentation, and resizing.
• If you are using these models, make sure to verify dataset compatibility.

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How to Use

Load the Model

from tensorflow.keras.models import load_model

# Load Brain Tumor Model
model = load_model("brain_model.h5")

# Predict on new images
import numpy as np
from tensorflow.keras.preprocessing import image

img_path = "sample_mri.jpg"
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

prediction = model.predict(img_array)
print("Tumor Detected" if prediction > 0.5 else "No Tumor")

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Performance Metrics

Model	Accuracy	Precision	Recall
Brain Tumor	95.2%	94.8%	96.1%
Breast Tumor	93.5%	92.7%	94.3%
Lung Tumor	96.1%	95.9%	96.8%

📌 Trained using TensorFlow/Keras on NVIDIA GPUs.

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Limitations & Future Work

🔹 Limited dataset coverage—may not generalize to all MRI variations.
🔹 Possible false positives/negatives in real-world cases.
🔹 Can be improved with transfer learning on hospital-specific datasets.

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Citation

If you use this model, please cite:

@misc{MRI_LLM,
  author = {Vijayendher Gatla},
  title = {MRI-Based Tumor Detection Models},
  year = {2025},
  url = {https://huggingface.co/wizaye/MRI_LLM}
}