Vijay Ramdev Sahani
Satyam Satanand Sahu
Abstract
Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Strategic
and well-thought-out treatment planning significantly contributes to improving a patient’s overall
quality of life. Many different imaging techniques, such as Magnetic Resonance Imaging (MRI),
Computed Tomography (CT) and also ultrasound are used to evaluate tumors in different parts of the
body, with a focus on using MRI images for brain tumors. It is difficult and time consuming to scan the
data generated by techniques such a MRI which is huge. To overcome this limitation, automated
classification methods are necessary to improve diagnosis and prevent deaths. The task of automatically
detecting brain tumors is particularly challenging due to the large spatial and structural variability of
the surrounding tissue. This study suggests the utilization of Convolutional Neural Networks (CNN) for
the automated detection of brain tumors. The CNN architecture is designed using small kernels and
neurons with small weights. The experimental findings demonstrate that the suggested approach
attained an impressive accuracy rate of 97.5% while maintaining a lower level of complexity in
comparison to leading-edge methodologies.
Keywords: Neural Networks ,MRI, Brain Image, convulutional networks, support vector machines
References
J. Seetha and S. Selvakumar Raja, “Brain Tumor Classification Using Convolutional Neural
Networks.”
2. Stefan Bauer et al, “Multiscale Modeling for Image Analysis in Brain Tumor Studies.”
3. Atiq Islam et al, “Multifractal Texture Estimation for Detection and Segmentation of Brain
Tumors.”
4. Meiyan Huang et al, “Brain Tumor Segmentation Based on Local Independent Projection Based
Classification.”
5. AndacHamamci et al, “Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR
Images for Radiosurgery Applications,” IEEE Transactions on Medical Imaging, 31(3) (2012).
6. Bjoern H. Menze et al, “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),”
IEEE Transactions on Medical Imaging, (2014).
7. Shamsul Huda et al, “A Hybrid Feature Selection with Ensemble Classification for Imbalanced
Healthcare Data: A Case Study for Brain Tumor Diagnosis,” IEEE Access, 4 (2017).
8. R. Karupputhan and V. Palanisamy, “Fuzzy based automatic detection and classification approach
for MRI- brain tumor,” ARPN Journal of Engineering and Applied Sciences, 9(12) (2014).
9. Jin Liu et al, “A Survey of MRI-Based Brain Tumor Segmentation Methods,” TSINGHUA Science
and Technology, 19(6) (2011).
10. Janani and P. Meena, “Image segmentation for tumor detection using fuzzy inference system,”
International Journal of Computer Science and Mobile Computing, 2(5): 244 – 248 (2013).
| Volume | |
| Received | September 1, 2023 |
| Accepted | September 5, 2023 |
| Published | March 28, 2024 |
