An Advanced Brain Tumor Segmentation using Integrated Deep Learning Approach

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : Poornachandu, Srinivas

10.14445/23488387/IJCSE-V11I11P101

How to Cite?

Poornachandu, Srinivas, "An Advanced Brain Tumor Segmentation using Integrated Deep Learning Approach," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 11, pp. 1-10, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I11P101

Abstract:

Accurate segmentation of brain tumors from medical imaging are essential for diagnosis and treatment planning. Deep learning has shown promise in automating this task, but challenges remain in achieving robust results. Several conventional models, like ANN, CNN, and fuzzy C-means, are used to segment the regions of the brain. However, most models face over-segmentation due to sensitive region data appearing in the MRI brain tumor image slices. The proposed models present a novel hybrid deep learning framework by integrating U-Net with DeeplabV3 to achieve better-segmented brain tumour segmentation accuracy, advancing the current state of the art. The experiment results showed better improvement than the conventional model in terms of accuracy and dice coefficients.

Keywords:

U-net, Deeplab V3, Data Augmentation, Fine tuning.

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