# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 1 Introduction
Similar to the present study, they used multiple datasets. The first dataset included 7023 images (1645 for meningioma, 1621 for glioma, 1757 for pituitary, and 2000 for normal classes), and the second dataset contained 253 images (155 brain tumors and 98 normal). Their suggested approach achieved an accuracy of 97.5%. The limitation of their study is that the interference time was too long (9 h). The crossover smell agent-optimized multilayer perception (CSA-MLP) was suggested for the identification and classification of a tumor by [PERSON] et al. (2018). Their images have been collected from three datasets, and their method was based on employing CNN+ MLP classification head, which was incorporated with the CSA optimization algorithm to enhance the accuracy. Their approach scored an accuracy of 98.56%. Although the authors combined CNN and higher hand-crafted features, only binary classification and holdout validation scenarios were employed. Various studies have been proposed in the literature with promising results, and this paper extends the existing work for brain tumor classification. Generally, most of the above-mentioned studies used a single dataset compared to our approach, where we integrated multiple datasets for system training and evaluated our method on an additional local dataset. The limited availability of annotated data combined with the small size may lead to overfitting and hinder the generalization of the proposed framework to diverse clinical scenarios. While pre-trained CNNs offer transferable features learned from large-scale datasets, their applicability to medical imaging tasks, particularly brain tumor characterization, may be constrained by domain-specific variations and complexities not adequately captured in general-purpose pre-trained models. The proposed framework utilizes an ensemble architecture as an automated tool for tumor detection and diagnosis. The developed architecture integrates multiple learnable modules with the ability to capture localized and long-range dependencies. Integrating those modules improves the system's ability to recognize complex patterns and facilitates the understanding of the context of features across the entire brain image, thus improving the system's performance. The main contribution aspects of the present work include (i) a robust ensemble architecture that integrates a weighted feature fusion of information-rich features for classification as compared to direct feature concatenation; (ii) prominent disease-related features are retained through three learnable modules compared to single feature-based methods; (iii) capturing of long-range dependencies, non-local relationships, and complex pattern recognition by integrating a ViT in addition to a localized CNN-basedmethod;and (iv) we documented an improved system classification accuracy using public local datasets via the utilization of a cross-validation evaluation scenario. The paper is structured into five sections, starting with the introduction of modern CAD systems for brain tumor detection and diagnosis, the relevant review of the recent and related literature work followed by the outlined contributions of this work in Section 1. Full descriptions of the data, the methodology, and the details of the learnable modules and features extractions strategies are completely given in Section 2. Employed evaluation criteria, conducted experiments, and obtained results are given in Section 4. A discussion of our results and associated conclusions as well as future work suggestions are outlined in Sections 5 and 6, respectively.