利用基于先进图像处理工具的人工智能技术进行早期乳腺癌检测

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zede Zhu, Yiran Sun, Barmak Honarvar Shakibaei Asli
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引用次数: 0

摘要

乳腺癌的早期检测对改善治疗效果至关重要,而人工智能(AI)与图像处理技术的最新进展已显示出在提高诊断准确性方面的巨大潜力。本研究探讨了各种图像处理方法和人工智能模型对早期乳腺癌诊断系统性能的影响。通过重点研究维纳滤波和总变异滤波等技术,我们旨在提高图像质量和诊断精度。本研究的新颖之处在于通过多个医学影像数据集对这些技术进行了全面评估,包括用于乳腺肿瘤图像分割和分类的 DCE-MRI 数据集(BreastDM),以及乳腺超声图像(BUSI)、乳腺图像分析协会(MIAS)、乳腺癌组织病理学图像(BreakHis)和乳腺放射摄影筛查数字数据库(DDSM)数据集。另一个关键方面是整合了先进的人工智能模型,如视觉转换器(ViT)和 U-KAN 模型(U-Net 结构与 Kolmogorov-Arnold 网络(KANs)相结合),为这些方法在不同成像环境中的功效提供了新的见解。实验表明,维纳滤波技术显著提高了图像质量,使用 BreastDM 数据集时,峰值信噪比(PSNR)达到 23.06 dB,结构相似性指数(SSIM)达到 0.79;使用 BUSI 数据集时,峰值信噪比(PSNR)达到 20.09 dB,结构相似性指数(SSIM)达到 0.35。当应用组合过滤技术时,结果各不相同,MIAS 数据集显示 SSIM 值下降,均方误差 (MSE) 增加,而 BUSI 数据集显示感知质量和结构保留得到增强。视觉转换器(ViT)框架在处理复杂图像数据时表现出色,尤其是在处理 BreastDM 和 BUSI 数据集时。值得注意的是,使用维纳滤波法处理 BreastDM 数据集的准确率为 96.9%,召回率为 96.7%,而组合滤波法则进一步提高了这些指标,准确率达到 99.3%,召回率达到 98.3%。在 BUSI 数据集中,维纳滤波器的准确率达到 98.0%,特异性达到 98.5%。此外,U-KAN 模型在乳腺癌病灶分割方面表现出色,在各种数据集上都优于 U-Net 和 U-Net++ 等传统模型,在 BUSI 数据集上的准确率为 93.3%,灵敏度为 97.4%。这些发现凸显了特定数据集预处理技术的重要性,以及 ViT 和 U-KAN 等先进人工智能模型显著提高早期乳腺癌诊断准确性的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools
The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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