Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification

• Published in: IEEE access Vol. 12; pp. 42868 - 42887
• Main Authors: Asiri, Abdullah A., Soomro, Toufique Ahmed, Shah, Ahmed Ali, Pogrebna, Ganna, Irfan, Muhammad, Alqahtani, Saeed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological neural networks; Brain; Brain cancer; brain tumor classification; brain tumor segmentation; Classification; Classification algorithms; Feature extraction; Image contrast; Image enhancement; image enhancement technique; Image segmentation; Independent component analysis; Machine learning; Magnetic resonance imaging; Magnetic resonance imaging (MRI); Medical imaging; Modules; Neural networks; Sensitivity; Support vector machines; Tumors; Wiener filtering
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3379136

Neurological and brain-related cancers are one of the main causes of death worldwide. A commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI), yet the manual evaluation of MRI images by medical experts presents difficulties due to time constraints and variability. This research introduces a novel, two-module computerized method aimed at increasing the speed and accuracy of brain tumor detection. The first module, termed the Image Enhancement Technique, utilizes a trio of machine learning and imaging strategies-adaptive Wiener filtering, neural networks, and independent component analysis-to normalize images and combat issues such as noise and varying low region contrast. The second module uses Support Vector Machines to validate the output of the first module and perform tumor segmentation and classification. Applied to various types of brain tumors, including meningiomas and pituitary tumors, our method exhibited significant improvements in contrast and classification efficiency. It achieved an average sensitivity and specificity of 0.991, accuracy of 0.989, and a Dice score (DSC) of 0.981. Furthermore, the processing time of our method, averaging at 0.43 seconds, was markedly lower compared to existing methods. These results underscore the superior performance of our approach over current state-of-the-art methods in terms of sensitivity, specificity, precision, and DSC. Future enhancements will seek to increase the robustness of the tumor classification method by employing a standardized approach across a suite of classifiers.