فهرست نشریات علمی دسترسی آزاد ایران

Background: Brain tumors are a specific disease that directly affects the brain. Magnetic Resonance Imaging (MRI) is considered the most effective imaging technique for diagnosing brain tumors, providing crucial information about tumor size, location, and type. However, accurately segmenting and extracting the tumor region from MRI images is a challenging task for radiologists and physicians, impacting the overall accuracy of diagnosis. Methods: This research focuses on addressing the challenges of brain tumor detection and segmentation in MRI images. In line with the recent trend of big data analysis, neuroimaging data, including MRI images, are considered an important subset of big data due to their volume, velocity, and variety. The proposed approach utilizes the Self Organizing Maps (SOM) Neural Network, a powerful concept in image processing, to handle noise and artifacts in brain MRI. Results: The proposed method employs image segmentation to focus on smaller parts of the brain and utilizes the SOM neural network for noise reduction, enhancing the processing of noisy brain images. The approach incorporates block processing to effectively approximate the suspected cancer zone, facilitating accurate medical diagnosis. The algorithm achieves precise specification of brain image zones by learning the unique SOM algorithm and setting an edge detection threshold. Experimental results demonstrate the superior performance of the proposed method, surpassing previous approaches, with a precision of over 10% in diagnosing abnormal brain areas. Conclusion: The study highlights the importance of MRI in brain tumor diagnosis and the challenges associated with accurate tumor segmentation. The proposed approach using the SOM Neural Network effectively addresses these challenges by reducing noise, enabling block processing, and enhancing the precision of tumor detection. Results indicate the potential of the proposed method to significantly improve brain tumor diagnosis and contribute to advancements in medical imaging for neuroimaging applications.