Garia Lalit, Muthusamy Hariharan
Department of Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar (Garhwal), 246174, Uttarakhand, India.
ECE Department, BTKIT Dwarahat, Almora, 263653, Uttarakhand, India.
J Imaging Inform Med. 2025 Apr;38(2):887-901. doi: 10.1007/s10278-024-01239-y. Epub 2024 Sep 3.
Thermography is a non-invasive and non-contact method for detecting cancer in its initial stages by examining the temperature variation between both breasts. Preprocessing methods such as resizing, ROI (region of interest) segmentation, and augmentation are frequently used to enhance the accuracy of breast thermogram analysis. In this study, a modified U-Net architecture (DTCWAU-Net) that uses dual-tree complex wavelet transform (DTCWT) and attention gate for breast thermal image segmentation for frontal and lateral view thermograms, aiming to outline ROI for potential tumor detection, was proposed. The proposed approach achieved an average Dice coefficient of 93.03% and a sensitivity of 94.82%, showcasing its potential for accurate breast thermogram segmentation. Classification of breast thermograms into healthy or cancerous categories was carried out by extracting texture- and histogram-based features and deep features from segmented thermograms. Feature selection was performed using Neighborhood Component Analysis (NCA), followed by the application of machine learning classifiers. When compared to other state-of-the-art approaches for detecting breast cancer using a thermogram, the proposed methodology showed a higher accuracy of 99.90% for VGG16 deep features with NCA and Random Forest classifier. Simulation results expound that the proposed method can be used in breast cancer screening, facilitating early detection, and enhancing treatment outcomes.
热成像术是一种通过检测双侧乳房之间的温度变化来在癌症早期阶段进行检测的非侵入性、非接触式方法。诸如调整大小、感兴趣区域（ROI）分割和增强等预处理方法经常被用于提高乳房热成像分析的准确性。在本研究中，提出了一种改进的U-Net架构（DTCWAU-Net），其使用双树复数小波变换（DTCWT）和注意力门来对正面和侧面视图热成像进行乳房热图像分割，旨在勾勒出潜在肿瘤检测的感兴趣区域。所提出的方法实现了平均Dice系数为93.03%和灵敏度为94.82%，展示了其在准确乳房热成像分割方面的潜力。通过从分割后的热成像中提取基于纹理和直方图的特征以及深度特征，将乳房热成像分类为健康或癌变类别。使用邻域成分分析（NCA）进行特征选择，随后应用机器学习分类器。与其他使用热成像检测乳腺癌的先进方法相比，所提出的方法在使用NCA和随机森林分类器的VGG16深度特征方面显示出更高的准确率，为99.90%。仿真结果表明，所提出的方法可用于乳腺癌筛查，有助于早期检测并提高治疗效果。
