A review of Brain Cancer Detection and Classification Using Artificial Intelligence and Machine Learning

Sanjukta Chakraborty

¹ Research Scholar, Department of Computer Science and Engineering, Seacom Skills University, Bolpur, W.B., India

Email: [email protected]

² Professor, Department of Computer Science and Engineering, Seacom Skills University, Kolkata, W.B., India

Email: [email protected]

*Corresponding Author

Brain cancer is a devastating and life-threatening disease that affects millions of individuals worldwide. Timely and accurate detection of brain tumors is crucial for effective treatment and patient outcomes. In recent years, there has been a growing interest in the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the detection and classification of brain cancer. The integration of AI and ML into medical imaging and diagnostic processes has shown remarkable potential in enhancing the accuracy and efficiency of brain tumor diagnosis. These technologies offer the capability to analyze complex patterns and structures within medical images, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, aiding in the early identification of brain tumors and the precise categorization of tumor types. This review aims to provide a comprehensive assessment of the current state of research in the field of Brain Cancer Detection and Classification using AI and ML. It delves into the methodologies, datasets, and performance metrics utilized in various studies. Additionally, it explores the challenges and limitations of existing approaches, ethical considerations. As the capabilities of AI and ML continue to evolve, understanding their potential in brain cancer diagnosis is of paramount importance. This review will not only summarize the achievements made thus far but also offer insights into the future directions and implications of integrating AI and ML in the critical domain of brain cancer detection and classification. As AI continues to evolve, it has the potential to revolutionize brain cancer treatment, ultimately improving patient outcomes and saving lives.

Brain Cancer, Brain Cancer Classification, Brain Cancer Detection, Machine Learning, Multi-Modal Images

Sanjukta Chakraborty, Dilip Kumar Banerjee (2024). A review of Brain Cancer Detection and Classification Using Artificial Intelligence and Machine Learning. Journal of Artificial Intelligence and Systems, 6, 146–178. https://doi.org/10.33969/AIS.2024060111.

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