Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis

Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI “Magnetic Resonance Imaging” brain image. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. In this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis.

• 1
  The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively.
• 2
  The TAE-PIS method demonstrates higher accuracy and efficiency in segmenting brain tumor glioblastomas from MRI images compared to traditional methods such as Genetic algorithms (GA), K-nearest neighborhood (KNN), and kernel-based support vector machine (K-SVM).
• 3
  Our utilization of Self-Organizing Maps (SOMs) for verification resulted in significant improvements, achieving an accuracy of 96.5% and reducing response times to 7.5 seconds.