Brain Tumor Classification using Convolutional Neural Networks (CNN) 🧠

Overview

This project implements a Convolutional Neural Network (CNN) to classify brain MRI scans into four categories: Glioma Tumor, Meningioma Tumor, Pituitary Tumor, and No Tumor. The model is designed to automate the tumor detection process, aiding radiologists in early diagnosis and treatment planning.

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Dataset

• Source: Brain Tumor MRI Dataset on Kaggle
• Size: Approximately 7,000 MRI images
• Classes: Glioma Tumor, Meningioma Tumor, Pituitary Tumor, No Tumor
• Format: JPG images, organized into training and testing sets

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Project Files

📂 Brain_Tumor_Classification_CNN

│── Brain Tumors and Mental Health.docx # A brief report to decode the connection between brain tumor and mental health

│── README.md # Project documentation

│── brain-tumor-classification-using-cnn.ipynb # Jupyter notebook for training and evaluation

│── brain-tumor-classification-using-cnn.pdf # PDF export of the notebook

│── brain-tumor-classification-using-cnn.html # HTML export of the notebook

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Key Features

✔️ Data Preprocessing – Resized MRI images, applied data augmentation, and normalized pixel values.
✔️ Transfer Learning – Utilized ResNet18 for feature extraction.
✔️ Custom Classification Layer – Fine-tuned the final layers for multi-class classification.
✔️ Model Evaluation – Calculated accuracy, precision, recall, and F1-score.
✔️ Visualization – Included confusion matrices and loss curves for performance analysis.

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Results

• Overall Accuracy: 91% on test data
• High Precision and Recall for Pituitary and No Tumor classes
• Confusion Matrix to visualize model performance across all classes

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Future Work

• Deploy the model as a web application for real-time tumor detection

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Contributors

• Gandhar Ravindra Pansare (Indiana University, Bloomington)
• Guided by Professor Krista Li

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License

This project is open-source under the MIT License.

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