Segmentation Method Of Deterministic Feature Clustering For Identification Of Brain Tumor Using Hybrid CNN

The Deterministic Feature Clustering Segmentation Method for Brain Tumor Identification using Hybrid Convolutional Neural Networks (CNN) increases the precision of tumor segmentation in MRI scans by combining convolutional neural networks with deterministic clustering algorithms. A state-of-the-art method that uses the You Only Look Once (YOLO) object detection framework to detect and categorize brain cancers in real-time is automated brain tumor segmentation and classification in MRI using YOLO-based deep learning. YOLO’s ability to perform object detection at high speed and accuracy makes it particularly suited for medical imaging tasks, where both segmentation (locating the tumor) and classification (identifying tumor type) are critical. The proposed method for brain tumor segmentation combines deterministic feature clustering with a hybrid CNN to improve the accuracy of tumor identification in MRI scans. The hybrid CNN extracts critical features from the images through convolutional layers that detect edges, textures, and patterns within the brain. The approach efficiently segments brain tumor regions, captures both global and fine details, and handles variations in tumor shapes and sizes, making it robust and generalizable across different MRI scans. The method is computationally efficient, as clustering reduces complexity by focusing on regions of interest post-feature extraction.