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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 03

Published on: March 2026 2026

BRAINAI: BRAIN TUMOR ANALYSIS AND PREDICTION

Manvi Tekriwal Akhilesh Madwalkar Kshiteej Salunke

Dr. Nilesh Uke

Indira College of Engineering and Management, Pune Savitribai Phule Pune University
DOI:https://doi.org/10.55041/ijcope.v2i3.002

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Abstract

Brain tumors are life-threatening neurological disorders that require timely and accurate diagnosis for effective treatment planning. Traditional MRI-based diagnosis depends heavily on expert radiologists, making the process time-consuming and susceptible to human error. Although deep learning models have shown high accuracy in brain tumor classification, most remain confined to research settings without real-time clinical integration. This paper reviews existing literature on AI-driven brain tumor detection and highlights the limitations of current systems, including lack of interpretability, limited deployment, and absence of automated reporting mechanisms. BrainAI is proposed as an intelligent diagnostic support system that leverages Convolutional Neural Networks and transfer learning models to classify MRI scans into multiple tumor categories while generating confidence scores and structured reports, thereby enhancing clinical efficiency and supporting informed medical decision-making processes.

How to Cite this Paper

Tekriwal, M., Madwalkar, A. & Salunke, K. (2026). Brainai: Brain Tumor Analysis and Prediction. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(03). https://doi.org/10.55041/ijcope.v2i3.002

Tekriwal, Manvi, et al.. "Brainai: Brain Tumor Analysis and Prediction." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i3.002.

Tekriwal, Manvi,Akhilesh Madwalkar, and Kshiteej Salunke. "Brainai: Brain Tumor Analysis and Prediction." International Journal of Creative and Open Research in Engineering and Management 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i3.002.

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References


  1. Amin, M. Sharif, M. Raza, and M. Yasmin, “Brain tumor classification using deep learning techniques,” Computer Methods and Programs in Biomedicine, vol. 208, pp. 106–123, 2021.

  2. Dhakshnamurthy, M. Govindan, K. Sreerangan, M. Nagarajan, and Thomas, “Brain tumor detection and classification using transfer learning models,” arXiv preprint arXiv:2401.XXXX, 2024.

  3. Mahmud, M. Mamun, and A. Abdelgawad, “A deep analysis of brain tumor detection from MR images using deep learning networks,” Algorithms, vol. 16, no. 2, pp. 1–18, 2023.

  4. A. Asiri et al., “Improved fine-tuned model based on CNN with ResNet50 and U-Net for brain tumor detection and segmentation,” Artificial Intelligence Applications in Medical Imaging, pp. 55–70, 2023.

  5. Hossain, A. Chakrabarty, T. R. Gadekallu, M. Alazab, and M. J. Piran, “Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1–12, 2024.

  6. H. Menze et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS),” IEEE Transactions on Medical Imaging, vol. 34, no. 10,

  7. 1993–2024, 2015.

  8. Z. Jun, “Brain tumor classification based on attention-guided deep learning model,” International Journal of Computational Intelligence Systems, vol. 15, no. 1, pp. 1–14, 2022.

  9. Soumick Chatterjee et al., “Classification of brain tumours in MR images using deep spatial models,” Scientific Reports, vol. 12, no. 1, pp. 1–15, 2022.

  10. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626.

  11. Banerjee and M. Jain, “Deep learning-based medical image analysis: Challenges and opportunities,” Biomedical Engineering Advances, vol. 4, pp. 100–115, 2023.

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  • •Published on: Mar 02 2026
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