Brain Tumor Detection and Classification using Deep Learning

Abstract: Brain Tumors are among the most serious and life-threatening diseases affecting the human brain and nervous system. Early detection is essential to improve patient survival and treatment outcomes. Traditional diagnosis mainly depends on manual examination of Magnetic Resonance Imaging (MRI) scans by radiologists, which can be time- consuming and may lead to human errors. To overcome these limitations, this research proposes a deep learning-based approach using Convolutional Neural Networks (CNN) for automatic detection and classification of brain tumors from MRI images.

The proposed system utilizes a publicly available brain MRI dataset, where preprocessing techniques such as image resizing, normalization, and data augmentation are applied to improve model performance and generalization. The model was implemented and trained using Google Colab with GPU support for faster computation. Experimental results demonstrate high classification accuracy along with strong precision, recall, and F1-score, indicating the effectiveness of the proposed system.

This study highlights the potential of artificial intelligence in improving medical diagnosis by making the detection process faster, more accurate, and less dependent on manual analysis. The proposed system can assist medical professionals in early tumor identification and has significant potential for future integration into real-time clinical applications.

Keywords: Brain Tumor Detection, MRI, CNN, Deep Learning, Transfer Learning, VGG16, Medical Image Analysis, Data Augmentation, Automated Diagnosis.