A Survey on Brain Tumor Prediction with Various Machine Learning Approaches

Brain tumor prediction plays a crucial role in the accurate diagnosis and timely treatment of this life-threatening medical condition. Traditional methods relying on manual analysis of medical images are subjective and time-consuming. Machine learning, particularly deep learning, has shown promise in improving brain tumor prediction. However, individual approaches may have limitations. This literature survey explores the emerging field of hybrid machine learning for brain tumor prediction. By combining multiple techniques, hybrid models aim to enhance prediction accuracy, robustness, and interpretability. The survey reviews recent research papers, analyzes techniques used in hybrid models, and assesses their performance against traditional approaches. The findings highlight the potential of hybrid machine learning in advancing brain tumor prediction for improved patient outcomes.