发布日期: 2025-02-12

更新日期: 2025-02-12

文章字数: 1.1k

阅读时长: 4 分

阅读次数:

⚠️ 以下所有内容总结都来自于大语言模型的能力，如有错误，仅供参考，谨慎使用
🔴 请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！
💗 如果您觉得我们的项目对您有帮助 ChatPaperFree ，还请您给我们一些鼓励！⭐️ HuggingFace免费体验

2025-02-12 更新

Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification

Authors:Pandiyaraju V, Shravan Venkatraman, Pavan Kumar S, Santhosh Malarvannan, Kannan A

Breast cancer is one of the leading causes of death globally, and thus there is an urgent need for early and accurate diagnostic techniques. Although ultrasound imaging is a widely used technique for breast cancer screening, it faces challenges such as poor boundary delineation caused by variations in tumor morphology and reduced diagnostic accuracy due to inconsistent image quality. To address these challenges, we propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification. We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework that dynamically adapts spatial mappings through morphological variation analysis, enabling precise pixel-level refinement of tumor boundaries. Subsequently, we introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net). Through a multi-level attention approach, the CSFEM magnifies distinguishing features of benign, malignant, and normal tissues. The proposed frameworks are evaluated against existing literature and a diverse set of state-of-the-art Convolutional Neural Network (CNN) architectures. The obtained results show that our segmentation model achieves an accuracy of 98.1%, an IoU of 96.9%, and a Dice Coefficient of 97.2%. For the classification model, an accuracy of 99.2% is achieved with F1-score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively.

乳腺癌是全球主要的死亡原因之一，因此迫切需要早期和准确的诊断技术。虽然超声成像是一种广泛用于乳腺癌筛查的技术，但它面临着由于肿瘤形态变化导致的边界不清以及图像质量不一致导致的诊断准确性降低等挑战。为了解决这些挑战，我们提出了用于乳腺病灶分割和分类的新型深度学习（DL）框架。我们引入了一种精度映射机制（PMM），用于精度映射和注意力驱动LinkNet（PMAD-LinkNet）分割框架，通过形态变化分析动态适应空间映射，实现对肿瘤边界的精确像素级细化。接着，我们针对组件特定特征增强分类器（CSFEC-Net）引入了一个组件特定特征增强模块（CSFEM）。通过多级注意力方法，CSFEM放大了良性、恶性和正常组织的区别特征。所提出的框架与现有文献和一系列先进的卷积神经网络（CNN）架构进行了评估。获得的结果表明，我们的分割模型达到了98.1%的准确率，IoU为96.9%，Dice系数为97.2%。对于分类模型，使用F1分数、精确度和召回率分别达到了99.2%、99.1%和99.3%，从而实现了99.2%的准确率。

论文及项目相关链接

PDF 27 pages, 18 figures, 6 tables

Summary

本文介绍了乳腺癌成为全球主要死亡原因之一的情况，强调了早期准确诊断技术的紧迫需求。针对超声成像在乳腺癌筛查中面临的挑战，如肿瘤形态变化导致的边界不清和图像质量不一致影响诊断准确性，提出了基于深度学习的乳腺病灶分割和分类新框架。通过精度映射机制和组件特定特征增强模块，实现对肿瘤边界的精确像素级细化和良、恶性及正常组织的特征增强分类。评估结果显示，所提出的分割模型准确率为98.1%，IoU为96.9%，Dice系数为97.2%；分类模型准确率为99.2%，F1分数、精确度和召回率分别为99.1%、99.3%和99.1%。

Key Takeaways