Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
目的通过应用深度学习CTBViT(Classification Transformer Block for TB)模型对耐多药肺结核和药物敏感性肺结核CT影像进行分类,探讨CTBViT模型分类效能。方法提出CTBViT模型,引入新的补丁缩减块(PRB),通过删除不重要的标记来提高效率,...目的通过应用深度学习CTBViT(Classification Transformer Block for TB)模型对耐多药肺结核和药物敏感性肺结核CT影像进行分类,探讨CTBViT模型分类效能。方法提出CTBViT模型,引入新的补丁缩减块(PRB),通过删除不重要的标记来提高效率,建立一种随机分类器,以避免将大型预训练模型应用于结核病数据集时遇到的过拟合问题,在数据集上评估模型性能,并与初级、中级、高级职称影像医生诊断效能比较。结果深度学习CTBViT模型的准确率为98.3%,分别高于初级、中级、高级职称的65.3%、68.1%、72.5%,差异均有统计学意义(χ^(2)=32.55、28.33、23.00,P均<0.001);测试时间为(0.50±0.01)分钟,分别快于初级、中级、高级职称影像医生测试时间(48.66±6.81)分钟、(46.00±6.24)分钟、(43.66±5.86)分钟,差异均有统计学意义(t=-67.462、-69.558、-70.259,P均<0.001)。结论CTBViT模型适用于耐多药肺结核病和药物敏感性肺结核的分类,它能尽早分类耐多药肺结核,有助于及时调整治疗方案,提高治疗效果。展开更多
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
文摘目的通过应用深度学习CTBViT(Classification Transformer Block for TB)模型对耐多药肺结核和药物敏感性肺结核CT影像进行分类,探讨CTBViT模型分类效能。方法提出CTBViT模型,引入新的补丁缩减块(PRB),通过删除不重要的标记来提高效率,建立一种随机分类器,以避免将大型预训练模型应用于结核病数据集时遇到的过拟合问题,在数据集上评估模型性能,并与初级、中级、高级职称影像医生诊断效能比较。结果深度学习CTBViT模型的准确率为98.3%,分别高于初级、中级、高级职称的65.3%、68.1%、72.5%,差异均有统计学意义(χ^(2)=32.55、28.33、23.00,P均<0.001);测试时间为(0.50±0.01)分钟,分别快于初级、中级、高级职称影像医生测试时间(48.66±6.81)分钟、(46.00±6.24)分钟、(43.66±5.86)分钟,差异均有统计学意义(t=-67.462、-69.558、-70.259,P均<0.001)。结论CTBViT模型适用于耐多药肺结核病和药物敏感性肺结核的分类,它能尽早分类耐多药肺结核,有助于及时调整治疗方案,提高治疗效果。
