Authors :
Bhumika Gupta; Tushar Choudhary; Vikas Kumar
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/3vyp2ujk
DOI :
https://doi.org/10.38124/ijisrt/25apr1181
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Brain tumours are one of the biggest threats to life-threatening cancers, and timely and accurate recognition is
critical for effective treatment planning and enhancing patient outcomes. Manual analysis of magnetic resonance imaging
(MRI) by radiologists is standard diagnostic practice, but it is often time-consuming and can lead to inter-observer
variability, leading to delayed or inaccurate diagnosis. In current investigation, we propose a folding deep learning (DL)
framework for neural networks (CNNs) recorded by MRI scans of automated brain tumor detection. This model was
developed using published data records containing either axis MRI images marked as Tumours or not tumor. Use
preprocessing techniques such as grey level gray levels, image size, and data expansion (rotation, flipping, zoom) to improve
model generalization and over-adaptation. This model is trained, verified and evaluated in a split of 80:20 train tests based
on accuracy, accuracy, recall and F1 scores. The proposed model achieves accuracy of over 95% and demonstrates its
effectiveness in distinguishing healthy brain tissue and tumor-related brain tissue. Furthermore, visualizations such as
confusion matrix and sample predictions provide insight into model's decision process. Future research will examine the
inclusion of tumor classifications of more complex architectures such as resets and efficient nets, including multiclass
classifications (such as glioma, meningioma, pituitary gland), and integration into real-time diagnostic systems.
Keywords :
Brain Tumor Detection, Deep Learning, Convolutional Neural Networks (Cnn), Mri Scans, Medical Image Classification, Tumor Diagnosis, Computer-Aided Diagnosis (Cad).
References :
- Afshar, P., Mohammadi, A., &Plataniotis, K. N. (2020). Brain tumor type classification via capsule networks. ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, 1360–1364. https://doi.org/10.110 9/ICASSP40776.2020.9052958
- Çiçek, Ö., et al. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. MICCAI 2016, 424–432. https://doi.org/10.1007/978-3-319-46723-8_49
- Deepak, S., & Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345. https://doi.org/10.1016/j.compbiomed.2019.103345
- Erickson, B. J., et al. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505–515. https://doi.org/10.1148/rg.2017160130
- Ferlay, J., et al. (2020). Global Cancer Observatory: Cancer Today. International Agency for Research on Cancer.https://gco.iarc.fr/today
- Hossain, M. S., Amin, M. A., & Mahdy, M. R. C. (2019). Brain tumor detection using convolutional neural network. International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–6. https://doi.org/10.1109 /ECACE.2019.8679221
- Isensee, F., et al. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203–211. https://doi.org/10.1038/s41592-020-01008-z
- Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
- Pereira, S., et al. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251. https://doi.org/10.1109/TMI.2016.2538465
- Sajjad, M., et al. (2019). Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science, 30, 174–182. https://doi.org/10.1016/j.jocs.2018.12.003
- Samek, W., Wiegand, T., & Müller, K.-R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296. https://arxiv.o rg/abs/1708.08296
- Zhang, Y., et al. (2011). Exudate detection in color retinal images for mass screening of diabetic retinopathy. Medical Image Analysis, 16(1), 30–46. https://doi.org/10.1016/j.media.2011.06.005
Brain tumours are one of the biggest threats to life-threatening cancers, and timely and accurate recognition is
critical for effective treatment planning and enhancing patient outcomes. Manual analysis of magnetic resonance imaging
(MRI) by radiologists is standard diagnostic practice, but it is often time-consuming and can lead to inter-observer
variability, leading to delayed or inaccurate diagnosis. In current investigation, we propose a folding deep learning (DL)
framework for neural networks (CNNs) recorded by MRI scans of automated brain tumor detection. This model was
developed using published data records containing either axis MRI images marked as Tumours or not tumor. Use
preprocessing techniques such as grey level gray levels, image size, and data expansion (rotation, flipping, zoom) to improve
model generalization and over-adaptation. This model is trained, verified and evaluated in a split of 80:20 train tests based
on accuracy, accuracy, recall and F1 scores. The proposed model achieves accuracy of over 95% and demonstrates its
effectiveness in distinguishing healthy brain tissue and tumor-related brain tissue. Furthermore, visualizations such as
confusion matrix and sample predictions provide insight into model's decision process. Future research will examine the
inclusion of tumor classifications of more complex architectures such as resets and efficient nets, including multiclass
classifications (such as glioma, meningioma, pituitary gland), and integration into real-time diagnostic systems.
Keywords :
Brain Tumor Detection, Deep Learning, Convolutional Neural Networks (Cnn), Mri Scans, Medical Image Classification, Tumor Diagnosis, Computer-Aided Diagnosis (Cad).
