# Model Name: NeuroNest3D

## Model Description

### Overview
NeuroNest3D is a deep learning model developed for precise brain image segmentation, leveraging the power of a nested 3D U-Net architecture. It's particularly adept at identifying and delineating various structures within brain MRI scans.

### Model Architecture
NeuroNest3D uses a nested version of the 3D U-Net architecture, characterized by its multi-level dense blocks that enhance feature extraction capabilities. Key architectural features include:
- Multiple convolutional layers with ReLU activation and 'same' padding for feature learning.
- MaxPooling layers for spatial down-sampling.
- UpSampling layers coupled with Conv2D layers for expanding the feature maps.
- Concatenation of feature maps from different stages to retain spatial information.
- A final Conv2D layer with softmax activation for multi-class segmentation.

The architecture is designed to capture complex spatial hierarchies in 3D medical imagery effectively.

### Training
- **Dataset**: Trained on the BraTS 2020 dataset, comprising diverse brain MRI scans with annotations.
- **Preprocessing and Data Generation**: Implemented using a custom DataGenerator class for efficient batch processing and augmentation.
- **Loss Function**: Utilizes a categorical cross-entropy loss function.
- **Metrics**: Includes accuracy, Mean IoU, and custom-defined dice coefficient metrics for each tumor type (necrotic, edema, enhancing).
- **Optimization**: Employs the Adam optimizer with a learning rate of 0.001.
- **Callbacks**: Early stopping, reduce learning rate on plateau, model checkpointing, and CSV logging for monitoring the training process.

### Performance Metrics
Performance evaluated using:
- Overall Dice Coefficient
- Per-class Dice Coefficients (Necrotic, Edema, Enhancing)
- Precision, Sensitivity, Specificity

## How to Load The Model
```python
  from huggingface_hub import from_pretrained_keras
  model = from_pretrained_keras("duzduran/NeuroNest3D")

{"license": "mit", "library_name": "keras"}
keras tensorboard license:mit has_space region:us