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Brain tumors are formed when abnormal cells grow within the brain or its surrounding tissues. Approximately 400 people in Ireland receive a primary brain tumor diagnosis each year. In the US, this number increases to almost 90,000 individuals diagnosed each year. Timely diagnosis of brain tumor is essential to saving lives and significantly reducing treatment costs. To automate this process, different Artificial Intelligence (AI) techniques have been adopted to identify brain tumors in humans. Specifically, various deep learning algorithms have been used to segment and classify brain tumors. In this paper, a systematic review is conducted based on Kitchenham & Charters methodology. We selected seven research questions to identify commonly used methods, datasets, features, metrics, and Explainable AI (XAI) approaches for AI-based analysis of brain tumors. This process starts by sourcing papers that address these techniques via the IEEE Xplore and ACM biblographic databases between January 2013 and December 2024. The papers are then filtered using specifically designed inclusion and exclusion criteria. Out of 3950 papers sourced from two electronic databases, only 101 papers were selected for this review. In summary, despite a focus on segmentation and classification, our findings indicate that no AI methods have been fully adopted in clinical practice. Furthermore, none of the reviewed papers address the specific problem of weakly-supervised brain tumor segmentation, highlighting a clear research gap in the existing literature that warrants further investigation. Also, only four articles on XAI were identified. Given the importance of transparency in network predictions for brain tumor analyses, this fact supports the need for more research in this domain.
Adegboro et al. (Sat,) studied this question.