A Survey on Brain Tumor Classification Using Machine Learning Techniques

Machine learning (ML) models have emerged as powerful tools in the field of medical diagnostics, particularly in the classification of brain tumors. These models leverage advanced algorithms to analyze complex patterns within medical imaging data, providing valuable insights for accurate and timely diagnosis. The application of ML in brain tumor classification holds immense promise for improving patient outcomes through early detection and tailored treatment plans. The success of machine learning models in brain tumor classification heavily relies on the quality and quantity of the data used for training. Medical imaging data, such as MRI scans, are commonly employed. Pre-processing steps, including normalization and augmentation, are crucial to enhance the robustness of models and ensure accurate classification. Additionally, the availability of well-curated and diverse datasets is essential for training models capable of handling variations in tumor characteristics. This paper presents a comprehensive survey on machine learning and deep learning models for brain tumor classification.