Improving Biomedical Image Segmentation: An Extensive Analysis of U-Net for Enhanced Performance

The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-validation, and kernel size modifications to conduct a systematic evaluation over man...

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Bibliographic Details
Published in2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) pp. 1 - 6
Main Authors Ahmed, Zuhair, Tanim, Sharia Arfin, Prity, Fariya Sultana, Rahman, Hasibur, Maisha, Tasmia Binte Munir
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.02.2024
Subjects
Advanced Optimizers
Biological system modeling
Biomedical Image Segmentation
Computer architecture
Data augmentation
GradCAM
Image segmentation
K-Fold Cross-Validation
Kernel Size Optimization
Measurement
Technological innovation
U-Net
Visualization
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Summary:The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-validation, and kernel size modifications to conduct a systematic evaluation over many folds. Significant gains in accuracy, precision, recall, F1- score and accuracy are indicate in the results which reach 96.70%. By lowering the reliance on data augmentation, the suggested modifications improve the model's effectiveness. Under resource constraints, our work provides important new under- standings for biological picture segmentation.
DOI:10.1109/ic-ETITE58242.2024.10493320