A Hybrid Technique for Brain Tumor Detection and Classification
Publisher: IEEE
Published in: 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)
My co-authors and I, present a comprehensive methodology aimed at early detection and accurate diagnosis of brain tumors. The urgency of early detection is underscored by the aggressive nature of brain tumors, which necessitates a multi-stage approach. Leveraging MRI technology, our methodology integrates advanced algorithms for detection, segmentation, and classification, complemented by a fused pre-processing method for optimal data preparation. By employing adaptive k-means segmentation and morphological operations, we enhance the accuracy of tumor localization and characterization. Additionally, our approach incorporates GLCM feature extraction and classification using a PNN, culminating in a robust framework for effective brain tumor analysis and treatment planning.