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: NeuralNetworks, MRI, Brain Image. convulutional neural networks, support vector machines
[This article belongs to International Journal of Biomedical Innovations and Engineering(ijbie)]
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| Volume | 01 |
| Issue | 02 |
| Received | September 1, 2023 |
| Accepted | September 5, 2023 |
| Published | September 25, 2023 |