A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images.

Objectives: Brain tumors have been a major factor in the development of mental disorders like anxiety and depression. The primary target of this research is to develop a smart, economically feasible system that can detect and classify brain tumors by analyzing MRI images, reducing manual labor, and shortening diagnostic time. Methods: Although diagnosis with medical imaging is efficient for various health issues, accurately classifying brain tumors remains challenging for medical experts. This research introduces a new energy channel-based hybrid optimized network (ECHO-Net) as an automatic identification tool for brain tumors, aiming to facilitate medical diagnosis remotely. Energy shape prior segmentation (ESPS) is part of the framework, providing accurate segmentation and cropping of tumor regions from MRIs. Channel and spatial attention-based neural network (CSA-Net) differentiates between normal and tumor-affected images. Additionally, the hybrid chimp-based whale optimization (HCWO) algorithm enhances prediction precision and optimally sets sigmoid activation function parameters for better convergence and generalization. Results: Performance evaluation of ECHO-Net using publicly available MRI brain datasets, including Figshare, BRATS 2018-2020, and a clinical MRI dataset, shows a peak signal-to-noise ratio of 41.87 dB, a Structural Similarity Index Measure of 0.992, a sensitivity of 99.4%, an accuracy of 99.2%, and an average computational time of 2.68 s, outperforming current state-of-the-art methods. Conclusion: The proposed ECHO-Net is an automated, accurate, fast, and robust system for brain tumor detection. With low computational costs, it effectively segments abnormal regions and recognizes tumor types, demonstrating strong potential as a tool in intelligent healthcare systems and real-world clinical applications.