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Title |
Tumor Segmentation in Multimodal Brain MRI Using Deep Learning Approaches |
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Author |
Waleed Al Shehri and Najlaa Jannah |
| Citation |
Vol. 22 No. 8 pp. 343-351 |
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Abstract |
A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain’s tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm. |
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Keywords |
Brain Tumor Segmentation, MICCAI BRATS, FLAIR, MRI Modalities, U-Net, Dice Coefficient. |
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