Rider-based CNN with AResU-Net Segmentation for Brain Tumor in 3D MRI image

Abstract

Magnetic Resonance Imaging is most widely utilized option for diagnosing the brain tumors, however accurate analysis of the brain tumors using 3D MRI scans is challenging because of the irregular tumor boundaries, poorly contrasted images and diverse shapes. Hence a novel Rider-based CNN with AResU-Net is introduced for the precise segmentation and classification of the brain tumors in 3D MRI image. Traditional methods struggles due to infiltrative tumors, diffused tumor boundaries, unclear and over-segmentation, leads to reduced classification accuracy. To address this challenge, a novel Attention-Based Residual U-Net with fuzzy clustering is introduced to enhance the segmentation by reducing the voxel errors in unclear 3D boundaries. The fuzzy clustering in the U-Net assigns membership values to pixels for ensuring smoother transitions across tissue boundaries and reducing the segmentation errors with unclear borders. Moreover, the existing CNN lacks in predicting the long-range characteristics because of fine-grained spatial data loss. To maintain long-term dependencies and enhance accuracy, a Rider optimization-based CNN-LSTM is introduced. During optimization, the CNN extracts the spatial features, while the LSTM handles long-term dependencies, with the Adaptive Rider Optimization (ARO) algorithm optimizing LSTM weights. The proposed model achieved superior performance, with a classification accuracy of 99.85% and sensitivity of 99.8%, thus mitigating the segmentation errors and addressing challenges in the brain tumor analysis.