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https://github.com/mohammad95labbaf/brain-tumor-transferlearning

The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍
https://github.com/mohammad95labbaf/brain-tumor-transferlearning

cnn cnn-keras deep-learning deep-neural-networks deeplearning kaggle-dataset keras keras-tensorflow neural-network neural-networks pretrained-models resnet-50 transfer-learning tumor-classification tumor-detection vgg16 vision vision-transformer vit

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Host: GitHub
URL: https://github.com/mohammad95labbaf/brain-tumor-transferlearning
Owner: mohammad95labbaf
Created: 2024-04-07T19:56:32.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-05-21T11:06:48.000Z (8 months ago)
Last Synced: 2024-05-21T12:36:46.629Z (8 months ago)
Topics: cnn, cnn-keras, deep-learning, deep-neural-networks, deeplearning, kaggle-dataset, keras, keras-tensorflow, neural-network, neural-networks, pretrained-models, resnet-50, transfer-learning, tumor-classification, tumor-detection, vgg16, vision, vision-transformer, vit
Language: Jupyter Notebook
Homepage:
Size: 746 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# Brain-Tumor-TransferLearning

### Dataset:
- The dataset utilized for this study is the **Brain Tumor MRI Dataset** sourced from Kaggle.
- It consists of a carefully curated collection of brain MRI scans specifically chosen to facilitate research in automated brain tumor detection and classification using the Keras library.
- The dataset aims to enhance diagnostic accuracy and includes a randomized subset (20% of the original data) categorized into 'yes' (tumor present) and 'no' (healthy) tumor classes for both training and validation purposes.

### Investigated Approaches:

1. **VGG16 + FC (VGG16 with Fully Connected Layers)**:
- VGG16 is a deep CNN architecture comprising 16 weight layers.
- It was pre-trained on the ImageNet dataset.
- Additional fully connected layers are incorporated for classification.

2. **VGG + CNN2D (VGG16 with Additional Convolutional Layers)**:
- This approach extends VGG16 by introducing more convolutional layers for enhanced feature extraction.

3. **ResNet50 + FC (ResNet50 with Fully Connected Layers)**:
- ResNet50, a deep residual network with 50 layers, addresses the vanishing gradient problem using skip connections.
- Fully connected layers are added for classification.

4. **ResNet50 + CNN2D (ResNet50 with Additional Convolutional Layers)**:
- Building upon ResNet50, this variant incorporates additional convolutional layers.

5. **ViT (Vision Transformer) + FC (Fully Connected Layers)**:
- Originally designed for natural language processing, ViT is a transformer-based architecture.
- It has been adapted for image classification by treating images as sequences of patches.
- Fully connected layers are employed for classification⁷.

### Investigation Objectives:
- Evaluate the effectiveness of transfer learning using pre-trained models (VGG16, ResNet50, and ViT) for brain tumor classification.
- Compare the performance of different architectures in terms of accuracy, sensitivity, specificity, and other relevant metrics.
- Gain insights into how transfer learning impacts model convergence, generalization, and robustness.

## Instructions for Kaggle API
1. **Download Kaggle API**:
- Install the Kaggle API by running `pip install kaggle`.

2. **Kaggle API Token**:
- Go to your Kaggle account settings and generate an API token.
- Save the token as `kaggle.json` in the root directory of this repository.

3. **Download Dataset**:
- Use the Kaggle API to download the dataset:
```
kaggle datasets download -d preetviradiya/brian-tumor-dataset
```

4. **Upload Kaggle API Token to Colab/Notebook**:
- If using Colab or Jupyter Notebook, upload the `kaggle.json` token to your environment.
- Use the following code snippet:
```python
from google.colab import files
files.upload()
```

5. **Unzip Dataset**:
- Unzip the downloaded dataset:
```
unzip brian-tumor-dataset.zip
```