Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Automated cancer detection systems are crucial for improving diagnostic accuracy and efficiency.
• Convolutional Neural Networks (CNNs) show significant promise in analyzing medical images like MRIs for tumor identification.

Purpose of the Study:

• To develop and evaluate an automated system for brain tumor detection and classification using CNNs on MRI images.
• To enhance the accuracy and speed of cancer diagnosis, thereby improving patient outcomes.

Main Methods:

• Utilized deep learning and image processing techniques, including image enhancement, segmentation, data augmentation, feature extraction, and classification.
• Developed a CNN-based model to accurately detect and classify tumors in MRI scans.
• Employed a hybrid approach combining traditional image processing with deep learning for robust analysis.

Main Results:

• Achieved high accuracy in detecting and classifying brain tumors from MRI images.
• Demonstrated the effectiveness of CNNs in learning complex features for medical image analysis.
• The system achieved 98.5% training accuracy, with high validation accuracy and low validation loss.

Conclusions:

• Deep learning techniques, particularly CNNs, offer a powerful tool for automating brain tumor detection from MRI images.
• The developed system can assist healthcare professionals in faster and more accurate cancer diagnosis, leading to better patient care.
• This approach has the potential to revolutionize medical image analysis and clinical workflows.