Authors : Tomeshwar; Komal Yadav; Sudhanshu S Dadsena

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/49p8u9da

DOI : https://doi.org/10.38124/ijisrt/25may2340

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The diagnosis of brain tumors using magnetic resonance imaging (MRI) remains a critical yet challenging task in the medical field. Traditional diagnostic procedures depend heavily on the expertise of radiologists, often resulting in delays and subjectivity. This research presents a Python-based automated framework that utilizes artificial intelligence and machine learning for accurate tumor detection. Key image features are extracted using Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradients (HOG), which are then classified using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The developed CNN model demonstrated high performance with an accuracy of 97.5%, indicating its viability for supporting clinical diagnostics through efficient and consistent tumor identification.

Keywords : Brain Tumor, MRI Analysis, Medical Image Processing, Deep Learning, CNN, SVM, GLCM, HOG, Python, AI in Medicine, TensorFlow, Keras, Scikit-Learn, OpenCV, Feature Extraction, Image Classification, Computer-Aided Diagnosis, Healthcare AI.

References :

1. Deepak, S., & Ameer, P. M. (2019). A deep learning approach with transfer learning for brain tumor classification. Computers in Biology and Medicine, 111, 103345.
2. Cheng, J., et al. (2015). Improved tumor detection using augmented tumor segmentation and classification methods. PLOS ONE, 10(10), e0140381.
3. Dalal, N., & Triggs, B. (2005). Gradient orientation histograms for detecting objects in images. In Proc. IEEE CVPR, 886–893.
4. Pedregosa, F., et al. (2011). Scikit-learn: A Python toolkit for machine learning. Journal of Machine Learning Research, 12, 2825–2830.
5. Abadi, M., et al. (2016). TensorFlow: Large-scale machine learning framework. In USENIX OSDI, 265–283.
6. Bradski, G. (2000). OpenCV: Real-time computer vision library. Dr. Dobb’s Journal.
7. Chollet, F. (2015). Keras: User-friendly deep learning library for Python. Available at https://keras.io
8. Navoneel Dataset on Kaggle. (n.d.). MRI images for brain tumor detection. Retrieved from https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
9. Litjens, G., et al. (2017). Review of convolutional neural networks in medical imaging. IEEE Transactions on Medical Imaging, 35(5), 1150–1160.
10. Lakhani, P., & Sundaram, B. (2017). Deep learning for detecting tuberculosis from chest X-rays. Radiology, 284(2), 574–582.
11. Shen, D., Wu, G., & Suk, H. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248.
12. Wang, S., & Summers, R. M. (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933–951.
13. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In CVPR, 4700–4708.
14. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In MICCAI, 234–241.
15. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A comprehensive review on deep learning in medical imaging. Medical Image Analysis, 42, 60–88.
16. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
17. Xu, Y., et al. (2017). Deep convolutional neural network for brain tumor segmentation in MRI images. Computers in Biology and Medicine, 95, 123–133.
18. Wang, G., et al. (2019). Deep learning for identifying metastatic breast cancer. Nature, 571(7764), 350–354.
19. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
20. Rezaei, M., & Banerjee, S. (2020). Deep learning for automated brain tumor classification using MRI scans. Computers & Electrical Engineering, 84, 106588.
21. Jaiswal, A., et al. (2020). Efficient deep learning model for brain tumor classification. Healthcare Technology Letters, 7(6), 169–175.
22. Tang, Y., et al. (2019). Automated brain tumor segmentation using CNNs with uncertainty estimation. Medical Image Analysis, 55, 36–46.
23. Zhang, Z., et al. (2018). Weakly supervised learning for brain tumor segmentation. Medical Image Analysis, 48, 28–42.
24. Roth, H. R., et al. (2015). Improving computer-aided detection using convolutional neural networks and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.
25. Litjens, G., et al. (2017). Deep learning in medical image analysis: Challenges and opportunities. Medical Image Analysis, 42, 60–88.
26. Tang, Y., et al. (2017). Multi-scale CNN for brain tumor segmentation. Medical Image Analysis, 45, 131–142.
27. Hu, Y., et al. (2019). Deep neural network based brain tumor segmentation: A survey. Neurocomputing, 346, 4–19.