A Survey on Brain Tumor Segmentation and Classification

Jemimma, T. A.; Vetharaj, Y. Jacob

doi:10.4018/ijsi.309721

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Based on these considerations, the evaluation of the proposed method is determined by using Eq. (9)(10)(11)(12)(13). In this study, we used Google Colab to implement all processes in each test scenario.…”

Section: Methodsmentioning

confidence: 99%

“…These efforts were put to get the best tumor classification/detection results. However, due to complex brain structures, tumor shapes and sizes that vary greatly, and the position of these brain tumors [12], it is necessary to build a CNN model specifically. The CNN model has several networks involving convolution processes with different kernel sizes.…”

Section: Introductionmentioning

confidence: 99%

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A New Voting of Convolutional Neural Networks for Brain Tumor Detection Based on MRI Images

2024

IJIES

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Detection of brain tumors based on magnetic resonance imaging (MRI) images is essential for follow-up examinations. Several CNN models have been proposed before to get the best performance in detecting brain tumors. However, it is still necessary to improve performance due to complex brain structures, varying tumor shapes and sizes, and the position of brain tumors. Therefore, we propose a new voting of convolutional neural networks (CNNs) based on MRI images to detect brain tumors. CNN in the proposed method has three network paths, each involving a convolution process with a different kernel size. Involving different MRI image input shapes on the proposed CNN can provide different detection results, so voting is needed for the final detection. We propose a voting method with the condition that if only one proposed CNN model with a particular input shape detects a brain tumor in the MRI image, the final detection result indicates a tumor in the image. To evaluate the method's performance, we used brain MRI image datasets for tumor detection arranged into training, testing 1 (small size), and testing 2 (large size). The test results on the dataset show that the proposed method yields the best accuracy of 99.24% for testing 1 and 99.92% for testing 2. With these results, our proposed method performed better than the other methods, including VGG16, VGG19, ResNet50, MobileNetV2, InceptionV3, and Xception.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Voting of Convolutional Neural Networks for Brain Tumor Detection Based on MRI Images

2024

IJIES

View full text Add to dashboard Cite

show abstract

“…For instance, in cases where the oedema region is disproportionately represented in comparison to the tumor region, the learning model may potentially amplify the morphological characteristics of the oedema, resulting in misclassification of the oedema as tumor tissue. [2] , [3] , [4] …”

Section: Introductionmentioning

confidence: 99%