Open Access Open Access  Restricted Access Subscription Access

Advancing Brain Tumor Diagnosis: The Role of Convolutional Neural Networks

P. Deepthi Chandan, Syeda Hifsa Naaz

Abstract


Brain tumor classification plays a vital role in early diagnosis and treatment planning, directly impacting patient survival. Traditional MRI-based diagnosis relies on radiologists, making it subjective and time-consuming. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized this process by automatically identifying tumors with high accuracy. CNNs analyze MRI scans to distinguish tumor types like gliomas, meningiomas, and pituitary adenomas, achieving accuracies above 98%. Techniques like transfer learning and data augmentation enhance performance, even with limited data.  However, challenges remain—dataset limitations, model interpretability, and high computational costs slow adoption. Future efforts must focus on clinical validation, ethical AI deployment, and federated learning for privacy. With these improvements, AI-driven diagnostics can support radiologists, ensuring faster and more reliable brain tumor detection.


Full Text:

PDF

References


Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ... & van Leemput, K. (2015). The multimodal brain tumor image segmentation benchmark (BraTS). IEEE Transactions on Medical Imaging, 34(10), 1993-2024.

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., ... & Menze, B. H. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BraTS challenge. arXiv preprint arXiv:1811.02629.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Topol, E. J. (2021). Deep learning-enabled medical computer vision. NPJ Digital Medicine, 4(1), 5.

Zhang, Y., Liu, H., & Hu, Q. (2020). Transferring pre-trained deep CNNs for brain tumor classification. Biomedical Signal Processing and Control, 57, 101819.

Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.

Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., ... & Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78.

Kurian, N. C., Joshi, D. M., & Hegde, A. (2022). Hybrid deep learning approaches for brain tumor classification using MRI images. Journal of King Saud University-Computer and Information Sciences, 34(6), 2423-2435.

Hussein, S., Gillies, R., Cao, K., Song, Q., & Bagci, U. (2017). Tumor classification using deep radiomic features extracted from multi-modal MRI scans. arXiv preprint arXiv:1711.10371.

Srihith, I. Venkata Dwaraka, I. Venkata Siva Kumar, R. Varaprasad, Y. Rama Mohan, T. Aditya Sai Srinivas, and Y. Sravanthi. "Future of Smart Cities: The Role of Machine Learning and Artificial Intelligence." South Asian Research Journal of Engineering and Technology 4, no. 5 (2022): 110-119.


Refbacks

  • There are currently no refbacks.