Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning.

Purpose: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical professionals. Methods: Here, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden index. Results: From these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, respectively. Conclusion: These findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis. [ABSTRACT FROM AUTHOR]