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一种用于精确乳腺癌检测的创新型热成像原型:整合压缩技术和分类方法。

An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods.

作者信息

Ahmed Khaled S, Sherif Fayroz F, Abdallah Mohamed S, Cho Young-Im, ElMetwally Shereen M

机构信息

Bio-Medical Department, Benha University, Benha 13518, Egypt.

Computers and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt.

出版信息

Bioengineering (Basel). 2024 Jul 29;11(8):764. doi: 10.3390/bioengineering11080764.

DOI:10.3390/bioengineering11080764
PMID:39199722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11352007/
Abstract

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates.

摘要

早期乳腺癌检测对于提高患者生存率至关重要。这项工作介绍了一种创新的热成像原型,该原型结合了受乳腺摄影设备启发的压缩技术。该原型提供无辐射且精确的癌症诊断。通过整合压缩和照明方法,热图像质量得到提高,分类准确性也有所提升。所建议的热成像设备的基本组件包括设备主体、板、电机、压力传感器、光源和热成像相机。我们使用SolidWorks软件2020包创建了该设备的3D模型。此外,分类研究使用了实验结果中的癌症图像和正常图像来验证所建议系统的有效性。我们对获得的数据集采用了预处理和分割方法。我们使用各种分类器成功地对热图像进行了分类,并检查了它们的性能。逻辑回归模型表现出色,准确率达到0.976,F1分数为0.977,精确率为1.000,召回率为0.995。这表明在正确分类与乳腺癌相关的热异常方面具有很高的准确性。所提出的原型是进行乳腺癌检测初步研究的高效工具,为早期诊断带来潜在进展,并提高患者生存率。

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