Multi-level brain tumor classification using hybrid coot flamingo search optimization Algorithm Enabled deep learning with MRI images.

Abstract

An innovative multi-level BT classification approach based on deep learning has been proposed in this article. Here, classification is accomplished using the SpinalNet, whose structure is optimized by the Hybrid Coot Flamingo Search Optimization Algorithm (CootFSOA). Further, a novel segmentation approach using CootFSOA-LinkNet is devised for isolating the tumour area from the brain image. Here, the input MRI images are fed into the Adaptive Kalman Filter (AKF) to denoise the image. In the segmentation process, LinkNet is used to separate the tumour region from the MRI image. CootFSOA is used to achieve structural optimization of LinkNet. The segmented image is then used to create several features, and the resulting feature vector is fed into SpinalNet to detect BT. CootFSOA is used in this instance as well to adjust the SpinalNet's hyperparameters and achieve high detection accuracy. If a tumour is detected, second-level classification is carried out by employing the CootFSOA-SpinalNet to classify the input image into several types, such as gliomas, pituitary tumours, and meningiomas. Moreover, the efficacy of the CootFSOA-SpinalNet has been examined considering accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) and has recorded superior values of 0.926, 0.931, and 0.925, respectively.