Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
Open accessAbstract
The accurate localization and classification of lung abnormalities from radiological images are important for clinical diagnosis and treatment strategies. However, multilabel classification, wherein medical images are interpreted to point out multiple existing or suspected pathologies, presents practical constraints. Building a highly precise classification model typically requires a huge number of images manually annotated with labels and finding masks that are expensive to acquire in practice. To address this intrinsically weakly supervised learning problem, we present the integration of different features extracted from shallow handcrafted techniques and a pretrained deep CNN model. The model consists of two main approaches: a localization approach that concentrates adaptively on the pathologically abnormal regions utilizing pretrained DenseNet-121 and a classification approach that integrates four types of local and deep features extracted respectively from SIFT, GIST, LBP, and HOG, and convolutional CNN features. We demonstrate that our approaches efficiently leverage interdependencies among target annotations and establish the state of the art classification results of 14 thoracic diseases in comparison with current reference baselines on the publicly available ChestX-ray14 dataset.
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There is a significant shortage of RT-PCR supply, which leads to delay in disease prevention efforts.
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BackgroundThe investigation of multiresolution approaches for chest X-ray image based COVID-19 detection
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Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images
Veeranjaneyulu, N., Shaik, R. K., Ketepalli, G., & Bodapati, J. D. (2021) · Traitement du signal · doi:10.18280/ts.380337 · Cited by 37 · see citing works
“A 3D deep CNN and a multi-level prediction system is suggested by Ho and Gwak [17] for the identification of lung nodules using the cube clustering and the multi-level cube estimation.”
MethodAltaf, F., Islam, S. M. S., & Janjua, N. K. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06044-0 · Cited by 36 · see citing works
“[19] proposed a technique for multiple feature integration.”
MethodNeural architecture search for pneumonia diagnosis from chest X-rays
Gupta, A., Sheth, P., & Xie, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-15341-0 · Cited by 37 · see citing works
“Ho et al. 17 proposed a localization approach using pre-trained DenseNet-121 and a classification based approach that integrates local and deep features to establish state of the art classification results on 14 thoracic diseases on the ChestX-ray14 dataset.”
MethodA Lightweight Deep Learning‐Based Pneumonia Detection Approach for Energy‐Efficient Medical Systems
Almaslukh, B. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5556635 · Cited by 32 · see citing works
“Another approach discussed was to use DenseNet-121 that had been trained before using the extracted features to identify different fourteen thoracic diseases [48].”
MethodDeep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans
Jain, D. K., Singh, T., Saurabh, P., Bisen, D., Sahu, N., Mishra, J., & Rahman, M. H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7474304 · Cited by 33 · see citing works
Zhao, X., & Wang, X. Y. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107018 · Cited by 21 · see citing works
On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
Okolo, G. I., Katsigiannis, S., Althobaiti, T., & Ramzan, N. (2021) · Sensors · doi:10.3390/s21175702 · Cited by 22 · see citing works
“[43] reported an accurate identification of 14 thoracic diseases using feature extraction techniques and the pretrained DenseNet-121 [44] model.”
MethodHossain, M. I., Zunaed, M., Ahmed, M. K., Hossain, S. M. J., Hasan, A., & Hasan, T. (2023) · IEEE Access · doi:10.1109/access.2023.3346315 · Cited by 21 · see citing works
Choudhry, I. A., Iqbal, S., Alhussein, M. A., Qureshi, A. N., Aurangzeb, K., & Naqvi, R. A. (2024) · Expert Systems · doi:10.1111/exsy.13750 · Cited by 23 · see citing works
“Khanh Ho and Gwak (2019) used a pre-trained DenseNet-121 model in combination with a range of local and deep characteristics that were extracted utilising methods including SIFT, GIST, LBP, HOGs and CNN to catego-rise 14 thoracic ailments.”
Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti
Bozkurt, F. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.898385 · Cited by 21 · see citing works
Feature-level ensemble approach for COVID-19 detection using chest X-ray images
Ho, T. K. K., & Gwak, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268430 · Cited by 20 · see citing works
“Second, we manually extracted handcrafted features using five types of local descriptors, radiomic features using data-characterization feature extractors with the essential step of lung segmentation, and deep features using pretrained ResNet18 on ImageNet and pretrained DenseNet121 on a 14-thoracic-disease-classifica…”
BackgroundMethodSelf-citationA hybrid deep convolutional neural network model for improved diagnosis of pneumonia
Mann, P. S., Panchal, S., Singh, S., Saggu, G. S., & Gupta, K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09147-y · Cited by 16 · see citing works
Kansal, K., Chandra, T. B., & Singh, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19350-1 · Cited by 16 · see citing works
Chehade, A. H., Abdallah, N., Marion, J.-M., Hatt, M., Oueidat, M., & Chauvet, P. E. (2024) · SN Computer Science · doi:10.1007/s42979-024-02751-2 · Cited by 17 · see citing works
MS-ANet: deep learning for automated multi-label thoracic disease detection and classification
Xu, J. M., Li, H., & Li, X. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.541 · Cited by 15 · see citing works
“In the fi eld of multi-label chest disease classi fi cation research, Ho & Gwak (2019) proposed a novel framework to integrate multiple features from both shallow and deep features, representative and discriminant features are extracted from open ChestX-ray14 data sets to distinguish 14 pathological…”
BackgroundA Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection
GUAIL, A. A. A., Jin-song, G., Olouladé, B. M., & Al-Sabri, R. (2022) · Sensors · doi:10.3390/s22083049 · Cited by 14 · see citing works
“To identify 14 thoracic diseases, researchers in [29] employed a localization strategy based on pre-trained DenseNet-121, as well as feature extraction, to identify the diseases.”
MethodDetection of COVID-19 Infection in CT and X-ray images using transfer learning approach
Tiwari, A., Tripathi, S., Pandey, D. C., Sharma, N., & Sharma, S. (2022) · Technology and Health Care · doi:10.3233/thc-220114 · Cited by 13 · see citing works
“Identification of thoracic diseases was made with the incorporation of DenseNet model [23].”
Albahli, S., & Nazir, T. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.955765 · Cited by 13 · see citing works
“Ho and Gwak (33) designed a two-stage approach for the precise identification of 14 different diseases from chest x-ray images.”
BackgroundMethodInfluentialAn Analysis of Pneumonia Prediction Approach Using Deep Learning
Pandey, A., Mehrotra, T., Kumar, R., & Saxena, S. (2023) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt57929.2023.10150688 · Cited by 12 · see citing works
“Jaiswal et al. suggested a Mask-RCCN-based model for diagnosing pneumonia from X-ray images of the chest in [8].”
MethodHorry, M. J., Chakraborty, S., Pradhan, B., Paul, M., Zhu, J. F., Loh, H. W., Barua, P. D., & Acharya, U. R. (2023) · Sensors · doi:10.3390/s23146585 · Cited by 23 · see citing works
Chang, J., Li, Y., & Zheng, H. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5491017 · Cited by 12 · see citing works
“Ho and Gwak [31] combined the four handcrafted features of scale invariant feature transform (SIFT); generalized search trees (GIST), local binary pattern (LBP), and histogram of oriented gradients (HOG) with the deep features extracted by CNN; and combined seven traditional supervised learning classifiers to implemen…”
AUTOMATIC DETECTION OF COVID-19 AND VIRAL PNEUMONIA IN X-RAY IMAGES USING DEEP LEARNING APPROACH
Tripathi, S., & Sharma, N. (2023) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237223500011 · Cited by 11 · see citing works
Automated thorax disease diagnosis using multi-branch residual attention network
Li, D., Huo, H., Jiao, S., Sun, X., & Chen, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-62813-6 · Cited by 11 · see citing works
Dwivedy, V., Shukla, H. D., & Roy, P. K. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108325 · Cited by 10 · see citing works
“Pre-trained DenseNet-121 along with feature extraction techniques were used by Ho and Gwak [18] in the precise identification of 14 thoracic disorders.”
MethodWeakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network
Meena, T., Kabiraj, A., Reddy, P. B., & Roy, S. S. (2023) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri58017.2023.00061 · Cited by 9 · see citing works
Yadav, Y., & Chand, S. (2021) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs51430.2021.9441864 · Cited by 8 · see citing works
“Ho and Gwak [13] used four features and pre-trained deep learning model DenseNet121 for deep feature detection.”
MethodArtificial Intelligence Empowering Medical Image Processing
Geroski, T., & Filipović, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-60044-9_7 · Cited by 9 · see citing works
COVID-19 Automatic Detection Using Deep Learning
Sanjalawe, Y. K., Anbar, M., & Al-E’mari, S. (2021) · Computer Systems Science and Engineering · doi:10.32604/csse.2021.017191 · Cited by 8 · see citing works
Almughamisi, N., Abosamra, G., AlBar, A. M., & Saleh, M. (2026) · Diagnostics · doi:10.3390/diagnostics16010159 · Cited by 7 · see citing works
“Ho et al. [31] enhanced the pre-trained DenseNet-121 using feature integration: Generalized Search Tree, Scale-Invariant Feature Transform, Histograms of Oriented Gradients, and Local Binary Patterns.”
Slide-Detect: An Accurate Deep Learning Diagnosis of Lung Infiltration
Mohamed, A. E., Fayek, M. B., & Farouk, M. (2023) · Data Intelligence · doi:10.1162/dint_a_00233 · Cited by 7 · see citing works
“The highest accuracies were achieved by Two-stream Collaborative Network [4], Dense Networks with Relative Location Awareness [13] and Multiple Feature Integration [2].”
BackgroundMethodInfluentialAzad, A. K., Mahabub-A-Alahi, M.-A.-A., Ahmed, I., & Ahmed, M. U. (2023) · Diagnostics · doi:10.3390/diagnostics13030574 · Cited by 7 · see citing works
Viral Pneumonia Detection Using Modified GoogleNet Through Lung X-rays
Ullah, M. S., Qayoom, H., & Hassan, F. (2021) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect53699.2021.9668553 · Cited by 7 · see citing works
Lumbantoruan, A. A., Bustamam, A., & Anki, P. (2021) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti54043.2021.9702861 · Cited by 6 · see citing works
Detecting Different Thoracic Disease Using CNN-Model
Tiwari, R., Verma, M., & Sar, S. K. (2022) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat53423.2022.9725940 · Cited by 6 · see citing works
COPD Multi-Task Diagnosis on Chest X-Ray Using CNN-Based Slot Attention
Jeon, W., Jang, H., Lee, H., & Choi, S. J. (2025) · Applied Sciences · doi:10.3390/app16010014 · Cited by 5 · see citing works
Multi-label classification of chest X-ray images with pre-trained vision Transformer model
Xing, S., Ju, Z., Liu, Z., Wang, Y., & Fuqiang, F. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220284 · Cited by 5 · see citing works
Geroski, T., Pavić, O., Dašić, L., Milovanovic, D. R., Petrovic, M. D., & Filipović, N. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-01018-0 · Cited by 6 · see citing works
Classification of Thoracic Abnormalities from Chest X-Ray Images with Deep Learning
Usman, N., Ashraf, M. U., Iqbal, M. J., Asaf, M. Z., Mir, M. M., Raza, U. A., & Sharif, B. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150402 · Cited by 5 · see citing works
“Transfer learning occurs when a network or model is trained on a dataset and a certain domain and then applied to train on a different dataset and task [23].”
BackgroundDetecting Pneumonia with a Deep Learning Model and Random Data Augmentation Techniques
Guesmi, T. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01405122 · Cited by 4 · see citing works
Lung diseases classification using pre-trained based deep learning model and support vector machine
Slama, A. B., Amri, Y., Barbaria, S., Rahmouni, H. B., & Trabelsi, H. (2025) · Polish Journal of Medical Physics And Engineering · doi:10.2478/pjmpe-2025-0021 · Cited by 3 · see citing works
An Overview on Covid19 Detection Algorithm Using Deep Learning
Taşçı, B. (2021) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact52855.2021.9696700 · Cited by 3 · see citing works
Sun, J., Hu, J., Wu, X., Xu, Z., Wang, Y., & Zhang, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109930 · Cited by 2 · see citing works
Wang, Y. (2022) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/cme55444.2022.10063276 · Cited by 2 · see citing works
“Feature extraction techniques using Deep Learning CNN models have been shown to accurately diagnose 14 distinct diseases [8].”
MethodTsaniya, H., Fatichah, C., & Suciati, N. (2023) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3608298.3608300 · Cited by 2 · see citing works
“Chest X-ray images contain dozens of patterns, corresponding to hundreds of underlying conditions [6].”
BackgroundDašić, L., Pavić, O., Geroski, T., Milovanovic, D. R., Petrovic, M. D., & Filipović, N. (2023) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe60311.2023.00035 · Cited by 2 · see citing works
“Pretrained DenseNet121 model was also used as one of the key components in the methodology for classification of 14 different thoracic diseases, proposed by Ho and Gwak [21].”
MethodPtbNet: Based on Local Few-Shot Classes and Small Objects to Accurately Detect PTB
yang, W., Gao, S., Zhang, H., Yu, H., Xu, M., Chong, P., Zhang, W., Wang, H., Zhang, W., & Qian, A. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3419134 · Cited by 2 · see citing works
“In [8], a pre-trained DenseNet-121 and feature extraction technique based on CNN architecture was used to identify 14 chest pathologies with an accuracy of 84.62%.”
InfluentialThakur, D. S., Mishra, A. M., Singh, J., Bhardwaj, V., & Gera, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125272 · Cited by 2 · see citing works
Muzakki, A. F., Prasetyo, S. Y., & Purwanto, E. S. (2025) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis65852.2025.10933107 · Cited by 2 · see citing works
“The fundamental idea behind HOG is that the gradient density distribution or edge directions inside an image can be used to depict the shape and appearance of an object [21].”
A Hybrid Deep Learning Approach for Enhanced Classification of Lung Pathologies From Chest X‐Ray
Sajed, S., Rostami, H., Garcia, J. E., Keshavarz, A., & Teixeira, A. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70227 · Cited by 2 · see citing works
Classification of pathologies on digital chest radiographs using machine learning methods
Aitimov, M., Shekerbek, A., Пестунов, И. А., Bakanov, G. B., Ostayeva, A., Ziyatbekova, G. Z., Mediyeva, S., & Omarova, G. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp1899-1905 · Cited by 2 · see citing works
“Ho and Gwak [14] precise localization and classification of lung anomalies on X-ray images are important for clinical diagnosis and treatment strategy.”
BackgroundViBaNet: A Novel Deep Learning Approach to Detect Bacterial and Viral Pneumonia
Hassan, F., Mehmood, M. H., Rahman, A. U., Khan, W., Khalid, S., & Ali, M. (2023) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit60620.2023.00026 · Cited by 2 · see citing works
Novel XRayNet framework for lung disease detection and infection region identificationPreprint
Nitha, V. R., & S, V. C. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1710296/v1 · Cited by 2 · see citing works
Muzakki, A. F., Prasetyo, S. Y., & Purwanto, E. S. (2025) · International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI) · doi:10.1109/icocseti63724.2025.11019966 · Cited by 1 · see citing works
“A feature vector is then created by merging all of the cell histograms [11], [13], [14].”
A Improved Training Method for Deep Learning Based Anatomical Classification of X-Rays
Arangarajan, P., Kumar, C. S., Shunmugakarpagam, N., Vijayabhasker, R., & Gayathri, C. (2023) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan58655.2023.10395633 · Cited by 1 · see citing works
Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
Horry, M. J., Chakraborty, S., Pradhan, B., Paul, M., Gomes, D. P. S., Ulhaq, A., & Alamri, A. (2021) · Sensors · doi:10.3390/s21196655 · Cited by 1 · see citing works
“Other recently published approaches include incorporation of label inter-dependencies via LSTM modules to improve prediction accuracy using statistical label correlations [24], augmenting deep learning with hand crafted shallow feature extraction [25] to improve classification accuracy over pure deep learning, and con…”
MethodRadiographic Image Processing Analysis for Lung Infection - A Review
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The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images
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Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images.Preprint
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Thoracic Image Analysis Using Deep Learning
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Pneumonia Detection Using Data Mining Techniques
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The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images
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Special Issue on Image Processing Techniques for Biomedical Applications
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Classification of seeds based on a novel multiple feature combination descriptor
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“achieving high-performance classification results of 14 thoracic diseases [7].”
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Detection and Classification of Chest Diseases using Machine Learning Algorithm
Aggarwal, R., Gupta, R., Jain, C., & Kumar, A. (2023) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3647444.3647947
“Khanh Ho and Gwak (2019) anticipated a technique for the arrangement of thoracic diseases in chest radiography using multiple feature integration [32].”
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SEDLF ‐ LDD : A Stacking Ensemble‐Based Deep Learning Framework for Lung Disease Diagnosis
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- Authors
- Ho, T. K., & Gwak, J.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Korea National University of Transportation (KR)
- Corresponding author
- Gwak, Jeonghwan
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2019-10-02
- Volume
- 9
- Issue
- 19
- Pages
- 4130
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2979001271
- Cited by
- 96
- References
- 59
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 12 · 2023: 20 · 2022: 16 · 2021: 20 · 2020: 14
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF-2019M3C7A1020406, NRF-2016M3C7A1905477, NRF-2017R1D1A1B03036423)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- thoracic disease classification, chest radiography, multi-label classification, weakly supervised learning, feature integration, handcrafted features, SIFT, gastrointestinal stromal tumor, local binary pattern, HOG, DenseNet121, ChestX-ray14
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