A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade...
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2025-06-01
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| author | Noppadol Maneerat Athasart Narkthewan Kazuhiko Hamamoto |
| author_facet | Noppadol Maneerat Athasart Narkthewan Kazuhiko Hamamoto |
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| description | Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations. |
| format | Article |
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| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-6193c9b2392e4794bc4596afaddeb06e2025-08-20T03:16:52ZengMDPI AGApplied Sciences2076-34172025-06-011513730010.3390/app15137300A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis DetectionNoppadol Maneerat0Athasart Narkthewan1Kazuhiko Hamamoto2School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandSchool of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandSchool of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, JapanTuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations.https://www.mdpi.com/2076-3417/15/13/7300atrous convolutionBayesian Optimizationchest radiographstuberculosis detectiondeep convolutional neural networksDeepLabv3+ |
| spellingShingle | Noppadol Maneerat Athasart Narkthewan Kazuhiko Hamamoto A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection Applied Sciences atrous convolution Bayesian Optimization chest radiographs tuberculosis detection deep convolutional neural networks DeepLabv3+ |
| title | A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection |
| title_full | A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection |
| title_fullStr | A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection |
| title_full_unstemmed | A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection |
| title_short | A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection |
| title_sort | cascade of encoder decoder with atrous convolution and ensemble deep convolutional neural networks for tuberculosis detection |
| topic | atrous convolution Bayesian Optimization chest radiographs tuberculosis detection deep convolutional neural networks DeepLabv3+ |
| url | https://www.mdpi.com/2076-3417/15/13/7300 |
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