Analysis of DL-Algorithms for Segmentation of Tumor
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Keywords:
Brain; Tumor; MRI; SegmentationAbstract
Precisely identifying brain tumors in MRI images is essential for medical image analysis, as it significantly aids in diagnosis, therapy planning, and tracking disease progression. Despite the rapid advancement of Deep learning methods, especially neural networks, which have added prominence for their effectiveness in image-related tasks, a systematic comparative analysis of leading DL architectures using the Figshare BTMR dataset is still lacking. This study addresses this gap by experimentally evaluating six prominent DL models—VGG16, VGG19, ResNet18, ResNet50, MobileNet, and Xception—used as backbone encoders for brain tumor segmentation. Each model’s performance is objectively measured using“metrics such as Mean Intersection over Union (mIoU) and Mean Dice Similarity Coefficient”(mDSC). The findings provide valuable insights into the balance between segmentation accuracy and efficiency, assisting in the selection of suitable models for real-time and clinical implementation. This work serves as a benchmark reference, guiding future efforts in selecting effective DL-based architectures for brain tumor segmentation tasks.
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