ﻻ يوجد ملخص باللغة العربية
We developed a deep learning model-based system to automatically generate a quantitative Computed Tomography (CT) diagnostic report for Pulmonary Tuberculosis (PTB) cases.501 CT imaging datasets from 223 patients with active PTB were collected, and another 501 cases from a healthy population served as negative samples.2884 lesions of PTB were carefully labeled and classified manually by professional radiologists.Three state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images. Transfer learning method was also utilized during this process. The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma and cavitary types simultaneously.Then the Noisy-Or Bayesian function was used to generate an overall infection probability.Finally, a quantitative diagnostic report was exported.The results showed that the recall and precision rates, from the perspective of a single lesion region of PTB, were 85.9% and 89.2% respectively. The overall recall and precision rates,from the perspective of one PTB case, were 98.7% and 93.7%, respectively. Moreover, the precision rate of the PTB lesion type classification was 90.9%.The new method might serve as an effective reference for decision making by clinical doctors.
Objective: We propose an end-to-end CNN-based locating model for pulmonary tuberculosis (TB) diagnosis in radiographs. This model makes full use of chest radiograph (X-ray) for its improved accessibility, reduced cost and high accuracy for TB disease
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time an
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Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based
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