Brain tumor classification using MRI images and deep learning techniques

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computational Biology

Background:

• Brain tumors present a significant diagnostic challenge requiring early detection and accurate classification.
• Current diagnostic methods can be time-consuming and prone to human error.
• Automated systems offer potential to improve accuracy and efficiency in brain tumor diagnosis.

Purpose of the Study:

• To develop and evaluate an automated brain tumor classification system using deep learning (DL) and Magnetic Resonance Imaging (MRI).
• To accurately detect and classify common brain tumors including glioma, meningioma, and pituitary tumors, alongside normal scans.
• To enhance diagnostic accuracy and facilitate early medical interventions.

Main Methods:

• Utilized a Convolutional Neural Network (CNN) architecture with VGG16 as the base model.
• Employed data augmentation techniques on diverse public datasets, totaling 17,136 brain MRI images.
• Developed a user-friendly web application for image upload and tumor prediction using HTML and Dash.

Main Results:

• Achieved a classification accuracy of 99.24%, surpassing existing benchmarks.
• The high accuracy is attributed to a large, diverse dataset, optimized network configuration, fine-tuning, and data augmentation.
• The developed web application demonstrated practical clinical utility for rapid tumor prediction.

Conclusions:

• The AI-driven system provides an efficient and reliable solution for brain tumor classification.
• The approach has the potential to significantly reduce diagnostic errors and improve patient care.
• This advancement in automated brain tumor detection promises improved patient outcomes through timely interventions.