Research Article
Harnessing the Combined Power of Artificial Intelligence and Machine Learning for Diagnosis of Brain Tumor
@INPROCEEDINGS{10.1007/978-3-031-77075-3_1, author={Jay Dholaria and Neetu Gupta}, title={Harnessing the Combined Power of Artificial Intelligence and Machine Learning for Diagnosis of Brain Tumor}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Oncology Brain Tumor Artificial Intelligence Machine Learning Convolution Neural Network}, doi={10.1007/978-3-031-77075-3_1} }- Jay Dholaria
Neetu Gupta
Year: 2025
Harnessing the Combined Power of Artificial Intelligence and Machine Learning for Diagnosis of Brain Tumor
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_1
Abstract
The process of diagnosing diseases stands as a pivotal conduit for converting observed clinical evidence into precise disease appellations. Brain cancer, characterized by the uncontrolled growth of abnormal cells within the brain or its associated structures, is a relentless and complex disease that continues to pose significant challenges in the field of oncology. This abstract provides a concise overview of ongoing research efforts aimed at advancing our understanding of brain cancer and developing innovative approaches for early detection and more effective therapeutic interventions. Focusing on the domain of Brain Cancer, the author explores the application of Machine Learning (ML), Artificial Intelligence (AI), and Soft Computing (SC) algorithms to enhance the detection and diagnosis of brain cancer. In conclusion, the objectives of these explorations transcend mere technical validation. Rather, they are intrinsically motivated by the quest to discern the pragmatic feasibility and clinical utility of these advanced algorithms. Amidst the uncertainty and complexity inherent to cancer diagnosis, these algorithms emerge as beacons of decision-making support. The crucible of risk is where they are evaluated, offering insights into the adaptability, robustness, and potential pitfalls associated with their application.
