Percolation Images: Fractal Geometry Features for Brain Tumor Classification

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computational Geometry

Background:

• Accurate brain tumor detection is vital for effective clinical diagnosis and treatment planning.
• Traditional methods may not fully capture complex spatial characteristics of tumors.
• Deep learning models require robust feature extraction for optimal performance.

Purpose of the Study:

• To develop a hybrid brain tumor classification framework combining fractal geometry and deep learning.
• To investigate the utility of fractal-based "percolation" images for enhancing tumor detection.
• To improve the accuracy and efficiency of automated brain tumor diagnosis.

Main Methods:

• A novel hybrid approach integrating fractal geometry and convolutional neural networks (CNNs).
• Generation of "percolation" images using fractal geometry concepts to highlight spatial features.
• Inputting both original and "percolation" images into a CNN for tumor classification.
• Validation using a widely recognized benchmark dataset for brain tumor imaging.

Main Results:

• The proposed hybrid method demonstrated enhanced performance in brain tumor classification.
• "Percolation" images significantly improved the system's ability to detect tumors.
• Experimental results on a benchmark dataset confirmed the effectiveness of the approach.

Conclusions:

• The integration of fractal geometry features, specifically "percolation" images, offers a valuable enhancement for deep learning-based brain tumor detection.
• This hybrid approach shows promise for improving the accuracy of automated medical image analysis in oncology.
• The findings suggest a potential pathway for more precise and efficient brain tumor diagnosis.