From Detection To The Segmentation Of Brain Tumors

Abstract

1. Introduction

Brain Tumor Detection and Segmentation Using Deep Learning

Encoder : The contraction path consist of several contraction blocks, each block takes an input that passes through res-blocks followed by 2x2 max pooling. Feature maps after each block doubles, which helps the model learn complex features effectively. Decoder : In decoder each block takes in the up-sampled input from prevoius layer and concatenates with the corresponding output features from the res-block in the contraction path. this is then passed through the res-block followed by 2x2 upsampling convolution layers this helps to ensure that features learned while contracting are used while reconstructing the image. Bottleneck : The bottleneck block, serves as a connection between contraction path and expansion path.The block takes the input and then passes through a res-block followed by 2 x 2 up-sampling convolution layers.

The output produced by the image segmentation model is called MASK of the image. Mask is presented by associating pixel values with their coordinates like [[0,0],[0,0]] for black image shape and to represent this MASK we flatten it as [0,0,0,0]

2. Objective

Comparision of deep learning models such as ResNet50, FineTuned VGG16, CNN for Brain Tumor Detection

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Aryavir07/From-Detection-To-The-Segmentation-Of-Brain-Tumors.git
cd From-Detection-To-The-Segmentation-Of-Brain-Tumors

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Aryavir07 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.