AL-BIRUNI EARTH RADIUS SPOTTED HYENA OPTIMIZER BASED TRANSFER LEARNING FOR BRAIN TUMOR CLASSIFICATION USING MRI

Brain tumor classification involves identifying and categorizing various types of tumors from Magnetic Resonance Imaging (MRI), a task critical for effective diagnosis and treatment planning. Existing methods often face challenges such as MRI noise, imprecise tumor localization, limited feature extraction, and high computational requirements, which can reduce classification accuracy and reliability. To address these challenges, an innovative technique named Al-Biruni Earth Radius Spotted Hyena Optimizer based Convolution Neural Network with Transfer Learning (BERSHO_CNN with TL) is proposed for classifying brain tumor. Initially, the pre-processing is carried out on MRI images using Non-Local Means (NLM) filtering. Tumor segmentation is then performed using the Multi-Attention Dense Network (MAD-Net). By utilizing the segmented images, feature extraction is conducted through a combination of 3D-Convolutional Autoencoder+Vision Transformer (3D-CAE+ViT). Finally, the brain tumors are classified using a proposed CNN with TL) model, which is trained using the BERSHO algorithm. BERSHO is developed by combining the Al-Biruni Earth Radius (BER) and the Spotted Hyena Optimizer (SHO). The proposed approach demonstrates enhanced classification accuracy, robust tumor localization, and improved feature representation compared to existing methods. The devised model has achieved a Negative Predictive Value (NPV) of 91.788%, accuracy of 92.847%, True Positive Rate (TPR) of 92.534%, Positive Predictive Value (PPV) of 92.277%, and True Negative Rate (TNR) of 91.747%. These findings indicate that the proposed framework provides a practical and efficient tool for automated brain tumor diagnosis, contributing new knowledge in integrating hybrid optimization with advanced feature extraction for improved MRI-based tumor classification.