Brain Tumor Detection Using Artificial Intelligence: A Review

Abstract

This paper focuses on the use of AI in brain tumor diagnosis by taking a look at MRI scans. More specifically, the research is based on deep learning models such as ConvNet i.e. Convolutional Neural Networks and several other algorithms of ML i.e. Machine Learning and describes the remarkable advances of AI in the field of diagnostic precision as well as the speed and efficiency of the techniques. One of the key edits that can profit from an AI-enabled solution is the diagnostics of brain tumors, including gliomas, meningiomas, and pituitary ones since their early discovery and exact classification is of paramount importance in certain treatment procedures. AI integration with the medical practice is considerable, as AI provides the solid foundation to radiologists not only decreasing risks related to human-driven mistakes but also providing aid in the diagnosing process. The paper also identifies some future directions in the field, namely, improving the interpretability of AI, increasing the data protection measures, and enlarging the training dataset to avoid the reproductions of discrimination and incrementing the modeling resilience. Recommendation for future studies include integrating ART with other diagnostic tools in order to foster a more sophisticated multi-faceted health care system. In sum, this study paints a hopeful picture of the role of AI in diagnosing and addressing oncology, postulating that future developments in the field of AI could gradually change the ability of diagnosing brain tumours and, therefore, enhance the quality of patients’ treatment.