Brain tumors (BTs), characterized by abnormal cell proliferation in the brain, lead to severe neurological symptoms and can be fatal if untreated. This review explores primary BTs such as gliomas, meningiomas, pituitary tumors, medulloblastomas, schwannomas, craniopharyngiomas, and primary central nervous system lymphomas, as well as secondary tumors from metastasis. In the medical field, one of the greatest difficult and time-consuming processes is BT detection and identification. There has been a significant growth in the total number of brain illnesses reported recently. To help doctors with early diagnostic and treatment measures, this has indirectly raised the need for automatic detection and identification systems. With regard to brain magnetic resonance imaging (MRI) identification and detection techniques, this article aims to provide a critical analysis of current trends. This research mainly aims to support the review and systematic analysis of hybrid computational intelligence methods that use machine learning (ML) and deep learning (DL) for BT and cancer diagnosis. The most important part of this research is the introduction of a unified comparative framework, which not only depicts existing methods but also points out present difficulties and gives future research directions for the construction of clinically interpretable and scalable diagnostic models. This research examines a variety of identification and detection techniques, beginning with the simplest ones and progressing to the most sophisticated methods including ML, DL, and hybrid approaches. The article presents a comprehensive review of the triad of methods consisting of ML, DL, and hybrid computational intelligence for the purpose of diagnosing BTs and cancer. One of the important aims is to provide proof of the effectiveness of the hybrid models that already exist while, at the same time, showing their capability of elevating the diagnosis’s precision and trustworthiness. The authors put forward a comparison framework that encompasses the three approaches—traditional, DL, and hybrid—in connection with their data preprocessing, model fusion, and performance aspects. The integration of various methodologies is aimed at providing a more transparent methodological perspective and at steering future research toward hybrid diagnostic systems that will be easier to understand, simple to scale up, and, ultimately, usable in hospitals. The pros and cons of BT detection and identification are discussed here. The review indicates that the hybrid techniques based on DL are more effective in the accurate classification of BTs. Research intends to make these technologies even better, thereby increasing the precision of diagnosis and the efficacy of treatment in neuro-oncology.
Jawar et al. (Thu,) studied this question.