A Novel Neuro-Based Strategy for Early Brain Tumor Diagnosis via NeuroTumorDetectNet (NTDN)

Abstract

Prompt identification of brain tumours is crucial for enhancing patient survival rates and optimising treatment approaches. Although there have been improvements in contemporary diagnostic methods, there are still difficulties in reaching high precision during the initial phases of tumour development. Introducing NeuroTumorDetectNet (NTDN), a new algorithm based on neurology that is specifically developed to detect brain tumours in their early stages, addressing these issues. By utilising sophisticated neural network structures, NTDN combines deep learning and neurobiological principles to precisely detect and forecast the initiation of brain tumours during their initial phases.

This research article conducts a comparative analysis of NTDN with other advanced methods, such as Convolutional Neural Networks (CNNs), Random Forest Classifiers (RFC), and Support Vector Machines (SVMs). Experimental results demonstrate that NTDN outperforms these conventional methods in terms of sensitivity, specificity, and early-stage detection rates. Unlike existing models, NTDN employs a multi-stage neuro-cognitive layer that mimics human neuro-signal processing, enhancing its predictive power for subtle abnormalities in brain imaging data. The superiority of NTDN is validated through comprehensive testing on multiple publicly available brain tumor datasets, showing a significant improvement in detection accuracy by up to 15% over traditional machine learning models. Furthermore, NTDN's ability to minimize false positives and detect small-scale tumors demonstrates its potential as a breakthrough in neuro-diagnostics. This research highlights the algorithm's impact on advancing early diagnostic methods, offering a promising solution for healthcare providers to improve brain tumor management and patient outcomes.