With the development of medical imaging technology and the introduction of computed tomography (CT), early screening for lung cancer is becoming more and more possible. In this paper, we introduce the method of wavelet dynamic analysis to extract and repair the lung parenchyma, so as to exclude the noise interference outside the lung parenchyma. The algorithm can help us to locate the lung nodules with higher accuracy. Then, the convolution neural network (CNN) optimized by genetic algorithm and the traditional CNN are used to extract the features of CT image of pulmonary nodules. The corresponding features of different images are automatically distinguished. By comparing the accuracy of the two algorithms, it is proved that the CNN optimized by genetic algorithm has higher accuracy. Finally, the CNN optimized by genetic algorithm is used to detect and classify the existing pulmonary nodule images, which provides guidance for CT image detection technology of pulmonary nodule.

BackgroundMycobacterium senegalense is a non-tuberculous mycobacterium and is found everywhere in the environment. However, M. senegalense infection in human is extremely rare, especially in immunocompetent individuals. It is difficult to detect M. senegalense infection because its symptoms are non-specific, and routine diagnostic tests are less sensitive. It is also resistant to commonly used antibiotics. Here, we report the first case of M. senegalense infection after laparoscopic cholecystectomy in China.Case PresentationA 55-year-old man was admitted because of repeated infections at multiple incision sites for more than 1 year. Although routine diagnostic test results were negative, metagenomic next-generation sequencing (mNGS) identified DNA sequences of M. senegalense in tissue samples from incision sites. The presence of M. senegalense was further confirmed by polymerase chain reaction and capillary electrophoresis. After 60 days of quadruple therapy with clarithromycin, moxifloxacin, rifampicin, and oxycycline, the patient's wound healed.ConclusionWe believe the case findings contribute to the limited amount of knowledge about M. senegalense infection and raises awareness that this infection can result in poor wound healing, even in an immunocompetent host. Owing to a lack of early, precise diagnosis, it is difficult to treat M. senegalense infections. Based on our findings, mNGS is a sensitive diagnostic test for M. senegalense infections.