Abstract
This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this study, 30 pulmonary tuberculosis patients and 27 pneumonia patients who were hospitalized were selected as the research objects, and they were divided into a pulmonary tuberculosis group and a pneumonia group. MRI examination based on noise reduction algorithms was used to observe and compare the signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) of the images. In addition, the apparent diffusion coefficient (ADC) value for the diagnosis efficiency of lung parenchymal lesions was analyzed, and the best b value was selected. The results showed that the MRI image after denoising by the deep convolutional neural network (DCNN) algorithm was clearer, the edges of the lung tissue were regular, the inflammation signal was higher, and the SNR and CNR were better than before, which were 119.79 versus 83.43 and 12.59 versus 7.21, respectively. The accuracy of MRI based on a deep learning algorithm in the diagnosis of pulmonary tuberculosis and pneumonia was significantly improved (96.67% vs. 70%, 100% vs. 62.96%) (P < 0.05). With the increase in b value, the CNR and SNR of MRI images all showed a downward trend (P < 0.05). Therefore, it was found that the shadow of tuberculosis lesions under a specific sequence was higher than that of pneumonia in the process of identifying tuberculosis and pneumonia, which reflected the importance of deep learning MRI images in the differential diagnosis of tuberculosis and pneumonia, thereby providing reference basis for clinical follow-up diagnosis and treatment.
Highlights
Pulmonary tuberculosis is a chronic infectious disease caused by conjugated mycobacteria
All patients underwent magnetic resonance imaging (MRI) examinations with a noise reduction algorithm. ere were 17 males and 40 females, and the age range of the patients was 18–73 years, with an average age of 37.54 ± 6.34 years. e diagnostic criteria were referred to the relevant diagnostic criteria in the Diagnostic Standards and Management Practices of Tuberculosis [13] and Differential Diagnosis of Pneumonia [14]. is study had been approved by the medical ethics committee of the hospital
MRI Imaging Results Based on Deep Learning Algorithm
Summary
Pulmonary tuberculosis is a chronic infectious disease caused by conjugated mycobacteria. It varies according to the degree of the disease and the range of the lesion. In the early stage of tuberculosis, it is difficult to find positive signs in a small range, and the percussion of patients with a wide range of lesions is presented with dullness, enhanced speech fibrillation, low alveolar breathing sounds, and wet rales [1]. Local contraction causes pleural collapse and mediastinal displacement [1]. In patients with tuberculous pleurisy, there are pleural frictional sounds in the early stage. E chest wall is full, percussion is turbidity and solid, and speech tremors and breathing sounds decrease or disappear when a large amount of pleural effusion is formed [2]. When the resistance is reduced or the cell-mediated allergy is increased, it may cause clinical disease. If timely diagnosis and reasonable treatment are given, most of them can be clinically cured, so improving diagnostic techniques is the premise of effective treatment of tuberculosis [3]
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