The diagnosis of brain tumors remains a complex

The gap

Evidence profile

Sourced from the future work and stated research gap of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 120 times in total.

Supporting evidence — 3 representative gaps

• AI -Based Novel Cognitive Computational Strategy for Optimizing Brain Tumor Classification Using Magnetic Resonance Imaging Data (2026) · International Journal of Creative and Open Research in Engineering and Management · doi

  9.1 CONCLUSION The Brain Tumor Classification System presented in this project demonstrates the effective use of deep learning and transfer learning techniques for medical image analysis. By leveraging EfficientNetB0 as a pre-trained backbone, the system is capable of accurately classifying MRI brain scans into four categories: glioma, meningioma, pituitary tumor, and no tumor. The approach combines robust feature extraction, fine-tuning, and data augmentation to achieve high accuracy while maintaining generalizability across previously unseen MRI images. The inclusion of histogram equalization as an enhancement technique provides better visual clarity for users without altering the input used for prediction, thus separating visualization from model training. The system’s three-column graphical user interface (GUI) enhances usability, allowing users to simultaneously view the original image, the enhanced version, and the classification results along with class probabilities. This interface ensures that even non-technical users, such as clinicians and students, can interact with the system efficiently. The design choices, including status indicators, probability displays, and interactive buttons, provide a seamless workflow from image upload to result interpretation. From a research perspective, the project validates the effectiveness of transfer learning for medical imaging, particularly in cases where labeled datasets are limited. The two-stage training process—first training only the top layers and then fine-tuning the deeper layers of EfficientNetB0—demonstrates how pretrained networks can be adapted to specific tasks with minimal risk of overfitting. Additionally, the use of modern image preprocessing and augmentation techniques enhances model robustness, making it more resilient to variations in MRI scans, such as differences in scanner settings, slice orientation, and patient anatomy. In terms of clinical relevance, this system has the potential to assist radiologists in early tumor detection, provide second-opinion support, and accelerate the diagnostic workflow. While the current model focuses on classification, the framework is extendable for tumor segmentation, multi-modal MRI analysis, and integration into hospital PACS systems. Future improvements, such as incorporating explainable AI techniques like Grad- CAM, will allow clinicians to visualize and validate model decisions, thereby increasing trust and adoption in real-world medical settings. Overall, the Brain Tumor Classification System represents a significant step toward AI-assisted diagnostic tools in neuro-oncology. It combines the strengths of modern deep learning architectures with a user-friendly interface, providing an effective, accessible, and accurate solution for brain tumor classification. Continuous dataset expansion, model refinement, and feature enhancements will further improve its performance and clinical utility, paving the way for a practical, deployable tool in medical imaging workflows. © 2026 The Author(s). Published by IJCOPE Journal. Website: https://ijcope.org/ 29 International Journal of Creative and Open Research in Engineering and Management ISSN: 3108-1754 (Online) Volume 02 Issue 04 April-2026 | Impact Factor: 3.5 FUTURE ENHANCEMENTS  Expand Dataset: Incorporate more diverse and larger datasets to improve model robustness and reduce class imbalance.  Multi-modal MRI Input: Include different MRI modalities (T1, T2, FLAIR) for more accurate tumor detection.  Explainable AI: Integrate Grad-CAM or other visualization methods to highlight tumor regions and improve interpretability.  Edge Deployment: Optimize the model for deployment on mobile or edge devices for real-time diagnosis.  Segmentation Module: Extend the system to not only classify but also segment tumor regions for surgical planning.  Continuous Learning: Implement online learning to update the model with new patient data over time.  Integration with PACS: Enable automatic fetching and classification of MRI scans directly from hospital PACS systems.  Enhanced GUI Features: Add batch processing, 3D visualization of MRI slices, and reporting tools for clinicians.

  generalfuture workevidence 5/5
  Keywords: tumor model system classification learning brain medical image techniques scans users visualization training interface clinicians
• Integrated Brain Tumour Detection: Cuckoo Optimization and SVD- Guided Segmentation with U-Net (2026) · SN Computer Science · doi

  Although the proposed COA–SVD–U-Net framework shows promising results for automated brain tumor seg- mentation from MRI images, several improvements can be Page 11 of 12 496 explored in future research. First, the model should be eval- uated on larger and more diverse datasets collected from multiple hospitals to improve its generalization capability in real clinical environments. Testing the framework on multi- institutional MRI datasets will help ensure robustness under different imaging conditions and patient variations. Second, future work can integrate the proposed optimi- zation strategy with more advanced deep learning architec- tures such as TransUNet and UNETR, which have recently shown strong performance in medical image analysis. Such integration may improve feature learning and segmentation accuracy. In addition, extending the current two-dimen- sional approach to volumetric segmentation using 3D U-Net could help capture three-dimensional tumor structures more effectively. Future studies may also explore multimodal MRI data such as T1, T1c, T2, and FLAIR images to obtain richer information about tumor tissues. incorporating explain- able AI techniques can improve model interpretability and increase trust among clinicians. Finally, optimizing the framework for real-time clinical deployment and validat- ing it with medical experts will help transform the proposed method into a practical computer-aided diagnosis system for brain tumor detection. Author Contributions All authors contributed significantly to this re- search work. Conceptualization and methodology: Ajay Singh Yadav, Navin Ahlawat. Mathematical modeling and algorithm design: Ajay Singh Yadav, Priyanka Agarwal. Software implementation and experi- ments: Anjali Malik, Anupam Swami, M. Vinothkumar. Data analysis and validation: Bhavani Viswanathan, Garima Pandey. Writing—orig- inal draft preparation: Ajay Singh Yadav, Anjali Malik. Writing—re- view and editing: All authors. Supervision: Ajay Singh Yadav. Funding The authors declare that no funding was received for this research work. This study was carried out as part of the authors’ aca- demic and institutional research activities without any financial sup- port from external funding agencies. Data Availability The datasets used in this study are publicly available. The primary dataset utilized is the BRATS (Brain Tumor Segmenta- tion) dataset, which can be accessed through official repositories. Additional processed data and implementation details supporting the findings of this study are available from the corresponding author upon reasonable request.

  generalfuture workevidence 5/5
  Keywords: tumor authors ajay singh yadav proposed framework brain future datasets improve help funding images model
• Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models (2025) · Cancers · cited 120× · doi

  The diagnosis of brain tumors remains a complex and time-consuming process that often requires specialized expertise and significant waiting times. There is a need for automation in the diagnostic process to improve patient outcomes. The study aims to address this gap by investigating the effectiveness of pre-trained deep learning models in classifying brain MRI images.

  generalstated research gapevidence 5/5
  Keywords: diagnosis brain tumors remains complex time-consuming process often

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