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Segmentation of ovarian cyst using improved U-NET and hybrid deep learning model
Multimedia Tools and Applications ( IF 3.0 ) Pub Date : 2023-10-11 , DOI: 10.1007/s11042-023-16998-z
Kamala C , Joshi Manisha Shivaram

The female reproductive system relies on the ovaries to produce eggs, but ovarian cysts can lead to complications such as torsion, infertility, and cancer, making it essential to diagnose them quickly. Ultrasound images are commonly used to detect ovarian cysts, but segmenting cyst regions from the surrounding tissue poses a challenge due to complex patterns and similar intensities. Few methods use the background's texture information to facilitate foreground segmentation. Ultrasound images include characters like speckle noise, low contrast appearance, and blurry boundaries that further complicate the task. Lesion shape and position variations exacerbate these challenges. This study proposes an improved deep learning-based segmentation technique using a database of ovarian ultrasound cyst images to overcome these issues. At the outset, the input has undergone pre-processing using non-sub-sampled contourlet domain-based cross-guided bilateral filtering (CGBF) and improved U-Net (IU-NET) for image segmentation. The presented architecture involved reducing the intricacy of U-Net through the alleviation of certain parameters. This resulted in a substantial acceleration of the learning process, by a factor of 100. To optimize the improved U-Net model, the Seagull Optimization Algorithm (SOA) was used. The algorithm helped to fine-tune the hyper-parameters of the U-Net architecture, including the batch size, learning rate, and epoch count, to achieve optimal performance. The optimization was performed by solving an objective function, which involved determining the dice loss coefficient (DLC) and weight cross-entropy (WCE). A numerical analysis was conducted, which demonstrated that the proposed methodology outperforms existing methods in terms of segmentation accuracy. The proposed model achieved a pixel accuracy of 99.36%, which was significantly higher than that achieved by existing methods.



中文翻译:

使用改进的 U-NET 和混合深度学习模型分割卵巢囊肿

女性生殖系统依赖卵巢产生卵子,但卵巢囊肿可能导致扭转、不孕和癌症等并发症,因此快速诊断至关重要。超声图像通常用于检测卵巢囊肿,但由于复杂的模式和相似的强度,从周围组织中分割囊肿区域提出了挑战。很少有方法使用背景的纹理信息来促进前景分割。超声图像包括散斑噪声、低对比度外观和模糊边界等特征,这些特征使任务进一步复杂化。病变形状和位置的变化加剧了这些挑战。这项研究提出了一种改进的基于深度学习的分割技术,使用卵巢超声囊肿图像数据库来克服这些问题。首先,使用基于非下采样轮廓波域的交叉引导双边滤波(CGBF)和改进的 U-Net(IU-NET)对输入进行预处理以进行图像分割。所提出的架构涉及通过减轻某些参数来减少 U-Net 的复杂性。这导致学习过程显着加速,提高了 100 倍。为了优化改进的 U-Net 模型,使用了海鸥优化算法 (SOA)。该算法有助于微调 U-Net 架构的超参数,包括批量大小、学习率和历元数,以实现最佳性能。通过求解目标函数来执行优化,其中涉及确定骰子损失系数 (DLC) 和权重交叉熵 (WCE)。进行了数值分析,结果表明所提出的方法在分割精度方面优于现有方法。该模型的像素精度达到了99.36%,明显高于现有方法。

更新日期:2023-10-12
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