An integrative framework for brain tumor segmentation and classification using neuraclassnet

Abstract

Technological developments in medical image processing have created a state-of-the-art framework for accurately identifying and classifying brain tumors. To improve the accuracy of brain tumor segmentation, this study introduced VisioFlow FusionNet, a robust neural network architecture that combines the best features of DeepVisioSeg and SegFlowNet. The proposed system uses deep learning to identify the cancer site from medical images and provides doctors with valuable information for diagnosis and treatment planning. This combination provides a synergistic effect that improves segmentation performance and addresses challenges encountered across various tumor shapes and sizes. In parallel, robust brain tumor classification is achieved using NeuraClassNet, a classification component optimized with a dedicated catfish optimizer. NeuraClassNet’s convergence and generalization capabilities are powered by the Cat Fish optimizer, which draws inspiration from the adaptive properties of aquatic predators. By complementing a comprehensive diagnostic pipeline, this classification module helps clinicians accurately classify brain tumors based on various morphological and histological features. The proposed framework outperforms current approaches regarding segmentation accuracy (99.2%) and loss (2%) without overfitting.