Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

By P. Lakhani, B. Sundaram. 2017

1,693 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Medical Imaging

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Citations in the corpus

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values (2018, methodology): “As DNNs get deployed in areas like medical diagnosis (Rajkomar et al., 2018) and imaging (Lakhani & Sundaram, 2017), reliance on explanations has grown.”
Deep learning predicts hip fracture using confounding patient and healthcare variables (2018, background): “Class imbalance can be partly mitigated by data augmentation strategies.(8,12,28,29,33) Islam et al.”
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017, methodology): “Additionally, few other works on TB screening has been conducted using the public datasets [8] and using additional data along with the public datasets [9, 10].”
Early prediction of circulatory failure in the intensive care unit using machine learning (2020, methodology): “ML techniques have been applied to tasks in radiology 44 , pathology 45 and critical care 46,47 in retrospective clinical studies.”
Pneumonia Detection Using CNN based Feature Extraction (2019, methodology): “Lakhani and Sundaram (2017) [12] proposed a method of detecting pulmonary tuberculosis following the architecture of two different DCNNs AlexNet and GoogleNet.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, positive): “The transfer learning strategy, which involves fine tuning of a network pre-trained on a different dataset, has been applied to a variety of tasks such as classification of prostate MR images to distinguish patients with prostate cancer from patients with benign prostate conditions [26], identificat”
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, positive): “In medical practice, DL is effective in image-centric specialties, proving itself by detecting pulmonary tuberculosis from chest radiographs and malignant melanoma from digital skin photographs (Lakhani and Sundaram, 2017; Esteva et al., 2017).”
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019, background): “Moreover, deep learning models present a promising performance in assisting lung cancer analysis [26 – 29].”