Brain Tumor Detection With Deep Learning

Abstract

1. Introduction

In recent years there has been growing popularity of Deep Learning in almost every fields where decision making is involved finance, health care, marketing, sales and what not. Deep Learning has shown promising results in the field of healthcare in many areas such as: Disease Diagnosis with Medical Imaging, Surgical Robots, Maximizing Hospital Efficiency. AI healthcare market is expected to reach $45.2 billion USD by 2026 from the current valuation of $4.9 billion USD. Deep learning has been proven to be superior in detecting diseases from X-rays, MRI scans and CT scans which could significantly improve the speed and accuracy of diagnosis.

In this project there was application of Deep Learning to detect brain tumours from MRI Scan images using Residual Network and Convoluted Neural Networks. This automatic detection of brain tumors can improve the speed and accuracy of detecting and localizing brain tumors based on MRI scans. This would drastically reduce the cost of cancer diagnosis and help in early detection of tumors without any human involvement and would essentially be a life saver. We have also compared the accuracy of results obtained by using two different models - ResNet50 and ResNet18 and used Transfer Learning to tune or freeze weights to evaluate what gives best result.There are 3929 brain MRI scans which are either positive or negative cases of brain tumour. Models were built using ResNet50 and ResNet18 and evaluated their performance in detecting positive or negative cases of brain tumors.

2. Objective

In this project there was application of Deep Learning to detect brain tumors from MRI Scan images using Residual Network and Convoluted Neural Networks. This automatic detection of brain tumors can improve the speed and accuracy of detecting and localizing brain tumors based on MRI scans. This would drastically reduce the cost of cancer diagnosis and help in early detection of tumors without any human involvement and would essentially be a life saver. We have also compared the accuracy of results obtained by using two different models - ResNet50 and ResNet18 and used Transfer Learning to tune or freeze weights to evaluate what gives best result.There are 3929 brain MRI scans which are either positive or negative cases of brain tumor. Models were built using ResNet50 and ResNet18 and evaluated their performance in detecting positive or negative cases of brain tumors.

This project demonstrates how Jupyter Notebook 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
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/PritomDas/Brain-Tumor-Detection-with-Deep-Learning.git
cd Brain-Tumor-Detection-with-Deep-Learning

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 PritomDas and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.