Brain Tumor Segmentation Using Deep Learning on MRI Images.

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Medicine
• Radiology

Background:

• Brain tumor (BT) diagnosis is complex and time-consuming, requiring expert radiologists.
• Increasing patient numbers and data volume strain traditional diagnostic methods, making them costly and inefficient.
• Deep learning (DL) offers promising automated solutions for rapid and reliable BT identification and segmentation.

Purpose of the Study:

• To develop and evaluate a deep learning model for accurate brain tumor segmentation and diagnosis using MRI images.
• To leverage the BraTS dataset for training and validating a convolutional neural network (CNN) for BT analysis.

Main Methods:

• Utilized a pre-trained deep convolutional neural network (CNN) model for brain tumor identification in MRI.
• Employed the Brain Tumor Segmentation (BraTS) dataset, comprising 335 annotated MRI images.
• Trained the CNN model using a categorical cross-entropy loss function and the Adam optimizer.

Main Results:

• The deep CNN model successfully identified and segmented brain tumors in the provided MRI dataset.
• Achieved a high validation accuracy of 98% for the brain tumor segmentation task.

Conclusions:

• Deep learning models, specifically CNNs, demonstrate significant potential for accurate and efficient brain tumor diagnosis and segmentation.
• The developed model shows promise in assisting radiologists and improving diagnostic workflows for brain tumors.