{"title":"使用基于注意力的修正 DenseNet 模型进行肺部疾病分类","authors":"Upasana Chutia, Anand Shanker Tewari, Jyoti Prakash Singh, Vikash Kumar Raj","doi":"10.1007/s10278-024-01005-0","DOIUrl":null,"url":null,"abstract":"<p><p>Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.\",\"authors\":\"Upasana Chutia, Anand Shanker Tewari, Jyoti Prakash Singh, Vikash Kumar Raj\",\"doi\":\"10.1007/s10278-024-01005-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.</p>\",\"PeriodicalId\":516858,\"journal\":{\"name\":\"Journal of imaging informatics in medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of imaging informatics in medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s10278-024-01005-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of imaging informatics in medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10278-024-01005-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/11 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
肺部疾病是对全球健康的重大威胁,影响着人们的健康和死亡率。计算机断层扫描(CT)和 X 射线成像等诊断程序在确定这些疾病方面发挥着关键作用。X 射线由于易于获取且价格低廉,是诊断肺部疾病的一种方便且经济的选择。我们提出的方法利用对比度受限自适应直方图均衡化(CLAHE)增强技术对 X 光图像进行增强,利用 DenseNet201 突出与肺部疾病相关的关键特征图。我们采用混合池化和通道关注机制增强了现有的 Densenet201 模型。实验结果表明,我们的模型优于著名的预训练模型,如 VGG16、VGG19、InceptionV3、Xception、ResNet50、ResNet152、ResNet50V2、ResNet152V2、MobileNetV2、DenseNet121、DenseNet169 和 DenseNet201。我们的模型在准确度、精确度、召回率和 F1 分数上分别达到了令人印象深刻的 95.34%、97%、96% 和 96%。我们还利用梯度加权类激活映射(Gradient-weighted Class Activation Mapping,Grad-CAM)对模型的决策过程进行了直观的分析,以识别正常、气胸和肺不张病例。我们的模型在热图方面的实验结果可以帮助放射科医生提高诊断能力和标记过程。
Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.
Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.