A Comprehensive Review on Optimization Algorithms for Brain Tumor Detection

Brain tumors originate in the brain and may develop malignant characteristics as they grow. The outcome has resulted in significant loss of life. Early diagnosis and effective treatment show crucial to conquer this disorder. The study recommended a method for brain (cerebral) tumor identification utilizing data cleaning, image partitioning, information retrieval, parameter tuning, and identification techniques. The brain tumor identification attains the precision of whale optimization utilizing convolutional neural network. The study provides an autonomous neural network architecture with best data integration system regarding cerebral typing tumors using axial images using EfficientNetB3. The study reveals a single way to implement accurate brain tumor multiple grouping and segmentation using MRI data, two-module computational system for brain tumor detection. Reviewing state-of-the-art methods and their performance. The paper seeks to provide comprehensive survey of optimization methods, machine learning, and deep learning. The previous studies in this survey also examine the comparative analysis of different approaches like preprocessing algorithms, feature extraction, datasets, model’s performance evaluation metrics, diagnostic challenges, computational cost, improvement techniques, hybrid models, artificial intelligence approaches, region accuracy, advanced structures, optimization routine, parameter tuning, and real-world relevance in clinical decision-making.