基于混合特征融合的超声波乳腺小结节分类框架

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-20 DOI:10.1186/s12880-024-01425-y
Mousa Alhajlah
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引用次数: 0

摘要

背景:乳腺癌是全球主要疾病之一。据美国国家乳腺癌基金会估计,2024 年预计将有超过 42,000 名妇女死于乳腺癌:乳腺癌的预后取决于乳腺微小结节的早期发现以及区分良性和恶性病变的能力。超声波检查是诊断该疾病的重要放射成像技术,因为它可以进行活组织检查和病变定性。使用者的经验和知识水平至关重要,因为超声诊断依赖于医生的专业知识。此外,计算机辅助技术还能减轻放射科医生的工作量,提高他们的专业知识水平,尤其是在医院病人较多的情况下:本研究介绍了用于诊断乳腺癌良性和恶性病变的混合 CNN 系统的开发过程。InceptionV3 和 MobileNetV2 模型是混合框架的基础。从这些模型中提取特征并逐个连接,形成一个更大的特征集。最后,各种分类器被应用于分类任务:结果:该模型使用 softmax 分类器取得了最佳效果,准确率超过 95%:结论:计算机辅助诊断极大地帮助了放射科医生,减轻了他们的工作量。因此,这项研究可以作为其他研究人员构建临床解决方案的基础。
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A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography.

Background: Breast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024.

Objective: The prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user's level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner's expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting.

Method: This work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task.

Results: The model achieved the best results using the softmax classifier, with an accuracy of over 95%.

Conclusion: Computer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
期刊最新文献
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