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, se...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1625 - 1642 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554113&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554113 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554113 179554113 179554113 10.1007/s10278-024-01005-0 179554113 ppf: 1625 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model. aug: au: Chutia, Upasana Tewari, Anand Shanker Singh, Jyoti Prakash Raj, Vikash Kumar affil: https://ror.org/056wyhh33 Department of Computer Science and Engineering, National Institute of Technology Patna, 800005, Patna, Bihar, India sug: subj: Lung Diseases Classification Lung Diseases Diagnosis Diagnostic Imaging Methods Models, Statistical Neural Networks (Computer) Sensitivity and Specificity Human Diagnosis, Respiratory System Thermography Descriptive Statistics Data Analysis Software Tomography, X-Ray Computed Experimental Studies ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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