MODEL PEMBELAJARAN MENDALAM HIBRIDA UNTUK SEGMENTASI BAGIAN-BAGIAN TUMOR OTAK PADA PEMINDAIAN MRI BERKUALITAS RENDAH Hybrid Deep Learning Model for Brain Tumor Sub-region Segmentation in Low-Quality MRI Scans

Pramudya, Andre (2024) MODEL PEMBELAJARAN MENDALAM HIBRIDA UNTUK SEGMENTASI BAGIAN-BAGIAN TUMOR OTAK PADA PEMINDAIAN MRI BERKUALITAS RENDAH Hybrid Deep Learning Model for Brain Tumor Sub-region Segmentation in Low-Quality MRI Scans. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

This study proposes a hybrid deep learning model for brain tumor segmentation in low-quality MRI images by combining V-Net and Swin Transformer architectures. MRI image segmentation poses a challenge in the medical field due to issues such as noise, resolution variations, and low contrast between healthy and tumor tissues. The study utilizes the MICCAI BraTS 2020 dataset, which comprises multimodal MRI images (T1, T1c, T2, and FLAIR) accompanied by segmentation labels. The research process involves preprocessing stages such as resampling, skull stripping, intensity normalization, and data augmentation. V-Net is employed to extract local spatial features from 3D volumetric data, while the Swin Transformer captures global spatial relationships through a self-attention mechanism. The segmentation results are subsequently refined using post-processing steps such as thresholding, morphological refinement, and false-positive removal. The model demonstrates strong segmentation performance, achieving Dice Scores of 0.8721 for Whole Tumor (WT), 0.8412 for Tumor Core (TC), and 0.8123 for Enhancing Tumor (ET). Additional evaluations using the Jaccard Index, sensitivity, and specificity further confirm the model's effectiveness. These results demonstrate that this hybrid approach significantly improves segmentation accuracy for low-quality MRI images and holds potential for clinical application in supporting brain tumor diagnosis.

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