Brain Tumor Detection Using Convolutional Neural Networks (CNN)

Why This Project?

The project uses advanced machine learning techniques to improve the accuracy and efficiency of diagnosing brain tumors from MRI scans. This is motivated by the need to assist radiologists in making faster and more reliable diagnoses, which can be crucial for the treatment and survival of patients with brain tumors.

How It Works

Our approach involves a convolutional neural network (CNN) that has been trained on a substantial dataset of MRI images. These images are preprocessed to enhance their quality and uniformity, making them suitable for the model to learn from. The CNN architecture is designed to extract features and patterns that are indicative of the presence of a tumor.

Tools Used

• PyTorch: An open-source machine learning library used to construct and train the CNN.
• OpenCV: Employed for image processing and preparing the dataset.
• NumPy: Utilized for numerical operations on arrays and matrices.
• Matplotlib & Seaborn: These libraries are used for visualizing the data and results, such as plotting the confusion matrix.
• Sklearn: Provided algorithms for machine learning tasks and evaluation metrics.

Outcome

The project successfully developed a CNN model that can classify MRI images with 100% accuracy. The model's performance was thoroughly evaluated using a range of metrics, including a confusion matrix and accuracy scores.

Achievements

• Developed a machine learning model capable of detecting brain tumors with a 100% accuracy rate in the testing phase.
• Streamlined the MRI analysis process, reducing the time required for diagnosis and potentially increasing throughput in medical imaging departments.
• Contributed an open-source tool to the medical community that can aid in the early detection of brain tumors.

How to Use This Project

1. Clone the repository to your local machine.
2. Install the required dependencies listed in requirements.txt.
3. Run the preprocessing script to prepare your MRI dataset.
4. Train the CNN model using the provided notebook.
5. Evaluate the model using the scripts to generate metrics and visualizations.

Future Work

• Expand the dataset with more varied MRI scans to further test and improve the model's robustness.
• Implement the model in a web-based application for easier access by healthcare professionals.
• Collaborate with medical institutions for clinical trials and further validation.