Chaotic Dynamic Walrus Optimization for Efficient Brain Tumor Classification in the Neurojico Era

Abstract

This paper presents a lightweight and computationally efficient deep learning framework for brain tumor classification, which reinterpreted through the lens of the Neurojico era, where algorithmic mediation and cognitive sovereignty redefine medical diagnostics. The proposed model integrates MobileNetV2 with a novel Chaotic Dynamic Walrus Optimization (CDWO) algorithm to achieve neuronal precision, reflecting a paradigm shift from human-exclusive diagnostic authority to hybrid cognitive systems. Finding brain tumors early and correctly is important for improving treatment and survival rates because they are one of the most dangerous conditions. AI-driven automation is necessary because interpreting large amounts of MRI data by hand takes a long time and is likely to make mistakes. The five-step model that this paper suggests includes getting images, preprocessing them, optimizing features, transfer learning, and testing performance. Experimental validation on a dataset of 3,264 MRI images demonstrates superior performance, achieving an accuracy of 94.3%, sensitivity of 94.79%, specificity of 96.43%, precision of 94.41%, and an F1-score of 94.46%. Along with these numbers, the results show a deeper epistemic shift: algorithmic logic is having a bigger effect on diagnostic authority, which raises questions about cognitive justice and algorithmic sovereignty. CDWO enhances global optimization by surpassing the existing optimal methodologies while maintaining sufficient energy efficiency for application in real-time clinical environments. In the Neurojico context, this model signifies both a technological advancement and a transition towards the algorithmic hospital, where human and machine cognition intersect, thereby challenging traditional notions of medical expertise and ethical responsibility.