College of Applied Computer Science, King Saud University, Riyadh, 11543, Saudi Arabia.
BMC Med Imaging. 2024 Sep 20;24(1):253. doi: 10.1186/s12880-024-01425-y.
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.
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.
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.
The model achieved the best results using the softmax classifier, with an accuracy of over 95%.
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.
乳腺癌是全球主要疾病之一。根据全国乳腺癌基金会的估计，预计 2024 年将有超过 42000 名女性死于该病。
乳腺癌的预后取决于乳腺微结节的早期检测以及区分良性和恶性病变的能力。超声检查是诊断该疾病的重要影像学技术，因为它可以进行活检和病变特征分析。用户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，计算机辅助技术通过潜在地减少放射科医生的工作量并提高他们的专业知识做出了重大贡献，尤其是在医院环境中面对大量患者时。
本工作描述了一种用于诊断良性和恶性乳腺癌病变的混合 CNN 系统的开发。InceptionV3 和 MobileNetV2 模型作为混合框架的基础。从这些模型中提取并单独连接特征，从而得到更大的特征集。最后，应用各种分类器进行分类任务。
该模型使用 softmax 分类器取得了最佳结果，准确率超过 95%。
计算机辅助诊断极大地帮助了放射科医生并减轻了他们的工作负担。因此，这项研究可以为其他研究人员构建临床解决方案提供基础。
