Implementation of 3d CNN-Based Brain Tumor Detection

Abstract

Brain tumors are life-threatening neurological disorders that require early and precise diagnosis for effective treatment. Traditional manual MRI interpretation is time-consuming and prone to human error, making automated AI-based detection essential. This project presents an AI-driven detection system for brain tumors using 3D Convolutional Neural Networks (3D CNNs) applied to MRI scans. The model processes 2D MRI slices, reconstructs a 3D brain model, and classifies the presence of tumors with high accuracy. The dataset undergoes preprocessing steps such as normalization, resizing, and augmentation to enhance model performance. The deep learning model is trained and evaluated using performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC to ensure reliable classification. The system also generates annotated 3D tumor visualizations, assisting radiologists in clinical decision-making. Experimental results show that the 3D CNN model significantly improves tumor detection accuracy, outperforming conventional 2D CNN approaches. This study highlights the potential of AI-based medical imaging for efficient, accurate, and automated brain tumor diagnosis in real-world clinical applications.