Automatic Tuberculosis Screening Using Chest Radiographs

By Stefan Jaeger, Alexandros Karargyris, Sema Candemir, Les R. Folio, Jenifer Siegelman, Fiona M. Callaghan, Z. Xue, Kannappan K. Palaniappan, Rahul Kumar Singh, Sameer Kiran Antani and 4 more. IEEE Transactions on Medical Imaging, 2013

689 citations recorded by the source corpus.

Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.

Subfields: Infectious Diseases, Media Technology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e7dd80c8090f15e1dc139626b3bc9c168353f1da

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Citations in the corpus

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare (2019, background): “Detection of diseases from X-ray images was examined in [24–26], classiﬁcations on image views from chest X-ray were carried out in [27], and body parts segmentation from chest X-ray images and computed tomography was performed in [23, 28].”
Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. (2014, methodology): “Initial classification results for both sets have been presented in (3), and lung segmentation results for the MC set are shown in (5), which can serve as benchmarks for other researchers.”
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017, methodology): “al [7] extracted several features from lung segmented CXRs and employed various classification methods to benchmark the features.”
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation (2016, background): “Detecting diseases from x-rays was demonstrated in [3, 45, 29], classifying chest x-ray image views in [63], and segmenting body parts in chest x-rays and computed tomography in [5, 21].”
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration (2013, neutral): “In the case of CAD-based identification of lung diseases, accurate lung boundary segmentation plays an important role in subsequent stages of automated diagnosis [8]–[10].”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “[169] 2022 Exploit concepts from experience repetition and adversarial generation research Cross-device Centralized Decentralized Simulation GAN FedAvg FedProx FedBN Synchronous Asynchronous Random X-ray data [170], [171] HAM10000 [172]”
Preparing a collection of radiology examinations for distribution and retrieval (2015, methodology): “We targeted outpatient studies because one use of the collection was to serve as a training set for automatic identification of abnormal chest images in an outpatient setting in Africa.(4) We used the accession numbers carried in the narrative reports to pull the corresponding chest x-ray images in ”