Enhancing Leukocyte Image Segmentation

Enhancing Leukocyte Image Segmentation. Using a Modified U-Net Architecture with Spatial Attention Mechanism Authors: Wahyudi Setiawan, Daulah Darien, Rosida Vivin Nahari, Alfonsus Junanto Endarta Affiliation: University of Trunojoyo Madura, Nemosys Co., Ltd. Conference: ICCGANT 2025.

The Crucial Role of Medical Image Segmentation. Image Segmentation A crucial process in medical image analysis, enabling precise identification of structures..

Addressing the U-Net's Spatial Detail Čap. Standard U-Net Limitations While widely used for semantic segmentation in medical imaging, the standard U-Net often fails to capture fine spatial details, limiting its precision..

Our Core Research Objectives. f Improve Segmentation Accuracy To significantly enhance the accuracy of leukocyte image segmentation, particularly for challenging nucleus and cytoplasm boundaries. 2 Evaluate Effectiveness To rigorously evaluate the effectiveness of our modified U-Net with Spatial Attention compared to the performance of the standard U- Net architecture..

Comprehensive Datasets for Training. Raabin-WBC. 1,145 images.

Methodology: Building Our Enhanced Model. Dataset Preprocessing Initial steps involved thorough dataset preprocessing to ensure consistency and optimal quality for model training. Base Model: U-Net We utilized the robust U-Net architecture as our foundational base model for image segmentation. Spatial Attention Integration A key modification was the seamless integration of Spatial Attention into the U-Net's encoder-decoder structure..

Methodology: Optimizing Model Training. Training Setup Parameters Optimizer: Adam Learning Rate: 0.0001 Batch Size: 8.

Breakthrough Results: Enhanced Performance. 0. 9435 Average Evaluation Score Our modified U-Net achieved an impressive average evaluation score, demonstrating superior performance. Significant Outperformance The U-Net with Spatial Attention significantly outperforms the standard U-Net across all metrics. Higher Accuracy & Robustness It demonstrates consistently higher accuracy and robustness in all tested scenarios, validating our approach..

Key Insights: The Power of Spatial Attention. Enhanced Focus Spatial Attention effectively enhances the model's focus on relevant cellular structures, particularly nucleus and cytoplasm..

Conclusion & Future Directions. Key Conclusion The integration of Spatial Attention is highly effective in boosting U-Net performance, leading to significant improvements in medical image analysis..