RCS YOLO

[MICCAI'23] Official implementation of "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection".

AI & Machine LearningPythonGPL-3.0

Abstract

RCS YOLO is an open-source AI & Machine Learning project. [MICCAI'23] Official implementation of "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection". This is the source code for the paper titled "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection" published in the Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), of which I am the first author. The paper is available to download from Springer or arXiv. It is built using Python, Computer Vision, Deep Learning, YOLO. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This is the source code for the paper titled "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection" published in the Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), of which I am the first author. The paper is available to download from Springer or arXiv.

2. Objective

[MICCAI'23] Official implementation of "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection".

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

4. Technology Stack

PythonComputer VisionDeep LearningYOLO

5. System Requirements

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

  • 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/mkang315/RCS-YOLO.git
cd RCS-YOLO
pip install -r requirements.txt

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 mkang315 and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship