# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 6 Conclusions  
This study successfully demonstrates the capability of an integrated deep-learning approach to enhance the accuracy and efficiency of meningioma detection and segmentation using MRI scans. The research findings underscore the effectiveness of employing convolutional neural networks combined with advanced classification algorithms, collectively contributing to more precise medical diagnoses and potentially better healthcare outcomes. The utilisation of diverse CNN architectures, as elaborated in Table 1, such as AlexNet, VGG16, GoogleNet, ResNet18, and ResNet50, highlights their distinct advantages in handling complex image processing tasks. Each architecture's specific features--from local response normalization in AlexNet to the innovative skip connections in the ResNet series--play a crucial role in improving the model's ability to classify accurately and segment brain tumours. These architectures are carefully chosen based on their performance metrics and suitability for the intricate requirements of medical image analysis, as reflected in their error rates and computational efficiency.  
The outcomes of this study, detailed in Table 3, reveal high accuracy, sensitivity, specificity, and Dice coefficient indices for the proposed method, particularly notable when applied to the BRATS 2018 dataset. These results validate the model's robustness and potential for clinical applications, providing a solid foundation for surgical planning and targeted therapies. The comparative performance analysis in Table 4 emphasises the superiority of our proposed model over existing methods. This superiority is a testament to the careful integration of CNNs with MobileNetV2 and Multi-Support Vector Machine classifiers, significantly enhancing tumour classification accuracy.  
However, the study also acknowledges challenges such as dataset bias, class imbalance, and the required high computational resources. These challenges necessitate refining segmentation and training techniques to ensure the models remain effective across diverse and realistic clinical scenarios. The research confirms that the strategic application of machine learning techniques, specifically tailored CNN architectures and ensemble learning strategies, can significantly improve the performance of MRI-based brain tumour classification systems. This advancement paves the way for more accurate and personalised diagnostic tools and promises to enhance therapeutic strategies for patients with brain tumours. Future work will focus on expanding these techniques to other forms of medical imaging and continuing to refine the models to handle the variability and complexity of real-world clinical data.  
Conceptualization, M.A., A.Z. and A.B.; methodology, A.B., A.Z. and B.M.; software, M.A.; validation, M.A., A.B. and A.Z.; formal analysis, R.Y.M.L.; investigation, M.A., A.B., B.M., R.Y.M.L. and A.Z.; resources, M.A. and A.B.; data curation, M.A. and R.Y.M.L.; writing--original draft preparation, M.A., A.B. and A.Z.; writing--review and editing, M.A., A.Z. and R.Y.M.L.; visualisation, M.A., A.B. and R.Y.M.L.; supervision, B.M. and A.Z.; project administration, B.M.; funding acquisition, B.M. and A.Z. All authors have read and agreed to the published version of the manuscript. The research was supported by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant no. of the research fund: AP14869848).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.