Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification

Abstract

Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology.