ABSTRACT
The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.
ABSTRACT
BACKGROUND: In recent years, the capacity of tumor cells to maintain high levels of glycolysis, even in the presence of oxygen, has emerged as one of the main metabolic traits and garnered considerable attention. The purpose of this meta-analysis is to investigate the prognostic value of glycolysis markers in liver cancer. METHODS: PubMed, Embase, and Cochrane Library databases were searched for articles on glycolytic marker expression levels associated with the prognosis of liver cancer until April 2023. Stata SE14.0 was used to calculate the aggregate hazard ratios and 95% confidence intervals. RESULTS: Thirty-five studies were included. The worse overall survival (OS) (P < 0.001), disease-free survival (DFS) (P = 0.001), recurrence-free survival (RFS) (P = 0.004), and time to recurrence (TTR) (P < 0.001) were significantly associated with elevated expression of glycolysis markers. Higher expression of PKM2 (P < 0.001), STMN1 (P = 0.002), MCT4 (P < 0.001), GLUT1 (P = 0.025), HK-2 (P < 0.001), and CA9 (P < 0.001) were significantly related to shorter OS. Increased levels of PKM2 (P < 0.001), CA9 (P = 0.005), and MCT4 (P < 0.001) were associated with worse DFS. Elevated PKM2 expression (P = 0.002) was also associated with poorer RFS in hepatocellular carcinoma patients. GLUT2 expression was not correlated with the prognosis of liver cancer (P = 0.134). CONCLUSIONS: Elevated expression of glycolysis markers was associated with worse OS, DFS, RFS, and TTR in patients with liver cancer. Therefore, these glycolysis markers could serve as potential prognostic markers and therapeutic targets in liver cancer. TRIAL REGISTRATION: PROSPERO registration: CRD42023469645.