Lung Disease Classification with Deep Learning Enhanced CNN Architecture in Chest X-Ray Imaging.
In this study, we introduce a robust method using a robust convolutional neural network (CNN) architecture for efficient segmentation and multi-classification of lung X-ray pathologies, by replacing the traditional max pooling with discrete wavelet transform (DWT), which provides more accurate down-...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 4; pp. 3159 - 3183 |
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| Autores principales: | , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
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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=196241826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196241826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2026 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196241826 189902655 196241826 196241826 10.1007/s10278-025-01760-8 196241826 ppf: 3159 ppct: 24 formats: tig: atl: Lung Disease Classification with Deep Learning Enhanced CNN Architecture in Chest X-Ray Imaging. aug: au: Slimani, Faiçal Alaoui Abdalaoui Bentourkia, M'hamed affil: Department of Medical Imaging and Radiation Sciences, 3001 12th Avenue North, J1H5N4, Sherbrooke, Qc, Canada sug: subj: Lung Diseases Classification Deep Learning Convolutional Neural Networks Radiography, Thoracic Image Processing, Computer Assisted Pneumonia Diagnosis COVID-19 Diagnosis Signal Processing, Computer Assisted Human Paired T-Tests One-Way Analysis of Variance Sensitivity and Specificity Generative Adversarial Networks Mathematics ab: In this study, we introduce a robust method using a robust convolutional neural network (CNN) architecture for efficient segmentation and multi-classification of lung X-ray pathologies, by replacing the traditional max pooling with discrete wavelet transform (DWT), which provides more accurate down-sampling and enhances the detection of fine lung structure details. Integrated with an advanced U-Net + + model and Attention Gates (AG), our method significantly improves lung segmentation accuracy. For lung pathology classification, we integrated DWT in the DenseNet-201 model to differentiate normal lung images from images with tuberculosis, pneumonia, and coronavirus disease 2019 (COVID-19). To address the challenges of limited and variable data, we employed the technique progressive growing generative adversarial network (PGGAN) data augmentation to generate realistic, high-resolution chest X-ray (CXR) synthetic images. This approach not only enriches the training dataset but also provides a nuanced representation of lung pathologies, enhancing the robustness and comprehensiveness of our diagnostic system. Our robust approach demonstrated strong and consistent performance in both segmentation and classification tasks within lung X-ray imaging diagnostics. In segmentation, it achieved on the Japanese Society of Radiological Technology (JSRT) dataset, with metrics such as 99.1% accuracy and 97.2% Dice coefficient, outperforming established methods like U-Net and U-Net + +. For the classification, it demonstrated notable improvements over DenseNet-201, especially in precision with an increase of 2.4% when data augmentation techniques were employed. These advancements suggest a significant step forward in accuracy and reliability for CXR image analysis, affirming our method's superior adaptability and potential in handling diverse and augmented datasets. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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