A synaptic deep tumor sense predictor system for brain tumor detection and classification

Accurate brain tumor detection is essential for prompt treatment and better patient outcomes. Nevertheless, segmentation errors and classification constraints plague current approaches. With the goal of improving brain tumor detection accuracy and dependability, this research holds hope for improved patient outcomes and a greater understanding of tumor pathology. This paper introduces a novel DeepNeuroXpert (DN-XPert) model to segment and classify brain tumors, accompanied by three other models: the NSAS-Net or Nested Self-Attentive Segmentation Network (NSAS-Net), AI2CF Classification that is Attention-Integrated Inception Capsule Fusion (AI2CF) Classification, and WPSO for Parameter Tuning which stands for Wombat-Pelican Synergy Optimization. Our research is focused on addressing the challenges in medical image analysis. We propose new ways to enhance precision and dependability of brain tumor detection using DN-Xpert, a technique that uses advanced deep learning methods for accurate segmentation and classification of tumors in the brain. Furthermore, the DN-Xpert and its partner models have shown high-level performance by exceeding benchmarks with accuracy that goes up to 99.4 % and precision, recall, and F1-score metrics reaching a maximum of 99 %. This outstanding accuracy emphasizes the potential power of our unique approaches in changing how we detect and classify brain tumors.