Accurate detection of brain tumor using optimized feature selection based on deep learning techniques.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computational Neuroscience

Background:

• Brain tumors are abnormal growths that disrupt brain function, posing a significant health risk.
• Timely detection and accurate diagnosis are crucial for effective brain tumor treatment.
• Current methods for detecting brain tumors face challenges in accuracy and time efficiency.

Purpose of the Study:

• To develop a novel, accurate, and optimized system for brain tumor detection.
• To address the limitations of existing image processing techniques in brain tumor identification.
• To improve the speed and precision of brain tumor diagnosis.

Main Methods:

• The system employs a multi-stage approach including preprocessing, segmentation, feature extraction, optimization, and detection.
• Preprocessing involves a compound filter (Gaussian, mean, median).
• Segmentation uses thresholding and histogram techniques, followed by Grey Level Co-occurrence Matrix (GLCM) for feature extraction.
• An optimized Convolutional Neural Network (CNN) with Whale Optimization and Grey Wolf Optimization is used for feature selection and classification.

Main Results:

• The proposed system achieved a high brain tumor detection accuracy of 98.9%.
• Performance was compared against other optimization techniques, demonstrating superior accuracy, precision, and recall.
• The system was implemented using the Python programming language.

Conclusions:

• The developed optimized CNN system offers a highly accurate and efficient solution for brain tumor detection.
• This approach significantly advances the field of medical image analysis for neurological disorders.
• The system's high accuracy suggests its potential for clinical application in early brain tumor diagnosis.