A Dual-Attention-ResUNet++ for Breast Tumor Segmentation using Ultrasound Images | ||||
| Nile Journal of Communication and Computer Science | ||||
| Volume 8, Issue 1, December 2024 PDF (870.27 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/njccs.2024.333992.1034 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Asmaa A. Hekal   1; Hossam El-Din Moustafa2; Hanan M. Amer2
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| 11Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt | ||||
| 2Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt | ||||
| Abstract | ||||
| This study introduces DAtt-ResUNet++, an advanced deep learning model specifically designed to enhance breast tumor segmentation in ultrasound images. The model integrates a Dual-Attention mechanism within the ResUNet++ framework, significantly improving its ability to focus on tumor regions while capturing relevant contextual information from surrounding tissue. By combining both spatial and channel-based attention, DAtt-ResUNet++ achieves higher segmentation precision. For evaluation, the model was rigorously tested on a public dataset of 780 breast ultrasound images, utilizing a robust 10-fold cross-validation approach. It achieved impressive results with a Dice similarity coefficient of 90.40 ± 0.88%, Intersection over Union (IoU) of 84.62 ± 1.12%, Sensitivity of 89.82 ± 0.75%, Precision of 93.44 ± 0.56%, and Accuracy of 98.73 ± 0.12%. These results position DAtt-ResUNet++ as a competitive tool against state-of-the-art methods, showcasing its potential to improve breast tumor segmentation in ultrasound imaging. Future research will explore further optimizations and validation on additional datasets. | ||||
| Keywords | ||||
| Deep Learning; Dual Attention Networks; Image Segmentation; Breast Ultrasound; Computer-Aided Diagnosis (CAD) | ||||
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