ETDHDNet: An advanced DenseNet-based extended texture descriptor for efficient tuberculosis prediction in CXR images

Asmaa Shati , Amitava Datta , Atif Mansoor , Ghulam Mubashar Hassan
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Abstract

Chest X-ray imaging (CXR) for the prediction of tuberculosis (TB) is essential in medical diagnostics, as it plays a vital role in early detection and the development of effective treatment plans. Although Convolutional Neural Networks (CNNs) are effective in extracting features from images in a hierarchical manner, they have limitation of texture-related features due to their emphasis on capturing global patterns. To address this shortcoming, we propose an Extended Texture Descriptor Histogram DenseNet (ETDHDNet), a model designed for TB prediction from CXR images by integrating texture analysis with deep feature learning. ETDHDNet consists of three key components: the Extended Texture Descriptor Histogram (ETDH) module to capture multi-scale texture features across fine, medium, and coarse granularities; a hierarchical feature learning unit with densely connected layers for extracting high-level features; and a neural network for handling binary and multi-class TB prediction. Experimental results demonstrate that ETDHDNet surpasses existing methods in TB prediction from CXR images across three datasets. For binary classification, the model achieves an AUC of 0.998 on TBX11K, 0.997 on the Tuberculosis Chest X-ray Database, and 0.930 on the Shenzhen Dataset, as well as an AUC of 0.993 for multi-class TB prediction on TBX11K.
ETDHDNet:一种先进的基于densenet的扩展纹理描述符,用于有效预测CXR图像中的结核
用于预测结核病的胸部x射线成像(CXR)在医学诊断中至关重要,因为它在早期发现和制定有效治疗计划方面发挥着至关重要的作用。卷积神经网络(Convolutional Neural Networks, cnn)虽然能有效地从图像中分层提取特征,但由于其强调捕获全局模式,在纹理相关特征方面存在局限性。为了解决这一缺点,我们提出了一种扩展纹理描述子直方图DenseNet (ETDHDNet)模型,该模型通过纹理分析和深度特征学习相结合,用于从CXR图像中预测结核病。ETDHDNet由三个关键组件组成:扩展纹理描述符直方图(ETDH)模块,用于捕获细、中、粗粒度的多尺度纹理特征;具有密集连接层的分层特征学习单元,用于提取高级特征;以及用于处理二元和多类结核病预测的神经网络。实验结果表明,ETDHDNet在三个数据集的CXR图像预测结核病方面优于现有方法。对于二元分类,该模型在TBX11K上的AUC为0.998,在TB胸片数据库上的AUC为0.997,在深圳数据集上的AUC为0.930,在TBX11K上的多类TB预测AUC为0.993。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligence-based medicine
Intelligence-based medicine Health Informatics
CiteScore
5.00
自引率
0.00%
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0
审稿时长
187 days
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