The Use of Artificial Intelligence Models in Brain Tumor Classification

Abstract

Brain tumor classification is very important for clinical diagnosis, but traditional methods like biopsy (which is invasive) and spectroscopy (which is expensive) make it hard to do well. Artificial Intelligence (AI) has become a helpful tool here, using medical images to make classification more accurate and efficient. This paper reviews AI models used for brain tumor classification. It looks at two main types: traditional machine learning models and deep learning models. The paper explains how these models work and finds that deep learning models usually work better than traditional ones because they can learn directly from data without extra manual steps. However, there are still problems. AI models, especially deep learning ones, are like “black boxes”. It is hard to understand how they make decisions. Also, they may not work well in real hospitals because of inconsistent medical records or limited equipment. What’s more, using patients’ sensitive data to train these models risks privacy leaks. Future research should add doctors’ professional knowledge to AI, help models adapt to different hospital settings, and use methods like federated learning to protect privacy. In short, AI has great potential in brain tumor classification, and solving these problems will help it be used more in hospitals to improve diagnosis efficiency.