Brain-Tumor-TransferLearning

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.

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.
VGG + CNN2D (VGG16 with Additional Convolutional Layers):
- This approach extends VGG16 by introducing more convolutional layers for enhanced feature extraction.
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.
ResNet50 + CNN2D (ResNet50 with Additional Convolutional Layers):
- Building upon ResNet50, this variant incorporates additional convolutional layers.
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.

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

Download Kaggle API:
- Install the Kaggle API by running pip install kaggle.
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.

Download Dataset:

Use the Kaggle API to download the dataset:

kaggle datasets download -d  preetviradiya/brian-tumor-dataset

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:
```
from google.colab import files
files.upload()
```
Unzip Dataset:
- Unzip the downloaded dataset:
```
unzip brian-tumor-dataset.zip
```