A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Brain tumors are a significant cause of mortality, necessitating early detection for improved patient outcomes.
• Tumor growth can increase intracranial pressure, damage healthy brain tissue, and lead to fatal consequences.
• Early diagnosis of brain tumors is crucial for increasing patient survival rates.

Purpose of the Study:

• To present a novel technique for accurate brain tumor detection and classification.
• To segment and classify both benign and malignant brain tumor cases effectively.
• To enhance the accuracy of tumor detection through advanced image processing and deep learning.

Main Methods:

• Application of spatial domain methods for image enhancement and segmentation.
• Utilization of AlexNet and GoogleNet (deep learning architectures) for tumor classification.
• Fusion of score vectors from softmax layers and integration with multiple classifiers.

Main Results:

• The proposed architecture demonstrates accurate segmentation and classification of brain tumors.
• Evaluation on benchmark datasets (MICCAI BRATS and ISLES) validates the model's performance.
• The technique effectively distinguishes between benign and malignant tumor types.

Conclusions:

• The developed technique offers a promising approach for early and accurate brain tumor diagnosis.
• This method has the potential to significantly improve patient survival rates through timely intervention.
• The integration of advanced image processing and deep learning provides robust tumor detection capabilities.