For the meter images collected in an actual environment, there is the possibility of tilt and rotation. This paper presents a method to calibrate the circular pointer-type meter based on YOLOv5s network. The convolutional neural network framework is used to detect the scale value in the meter panel as the key point. The position information and value information of the detected scale value are used to fit the elliptic equation of the position of the scale value with the least square method for perspective correction and rotation correction of the meter, and the corrected meter image is used to obtain the meter pointer reading. This paper proposes the weighted angle method to read the meter reading. After multiple transformations, the accumulated error of the meter image is eliminated. Finally, comparing the pointer detection method of this paper with the traditional pointer detection method, the error of this detection method is smaller; comparing the meter reading results before and after correction, the meter reading error after correction is 50% less than before correction. Comparing the method in this paper with other mainstream methods, it proves the effectiveness of the our method.

Most detection methods of coronavirus disease 2019 (COVID-19) use classic image classification models, which have problems of low recognition accuracy and inaccurate capture of modal features when detecting chest X-rays of COVID-19. This study proposes a COVID-19 detection method based on image modal feature fusion. This method first performs small-sample enhancement processing on chest X-rays, such as rotation, translation, and random transformation. Five classic pretraining models are used when extracting modal features. A global average pooling layer reduces training parameters and prevents overfitting. The model is trained and fine-tuned, the machine learning evaluation standard is used to evaluate the model, and the receiver operating characteristic (ROC) curve is drawn. Experiments show that compared with the classic model, the classification method in this study can more effectively detect COVID-19 image modal information, and it achieves the expected effect of accurately detecting cases.

Pointer-type meters are widely used in military, industrial, and aerospace applications. In this paper, we propose a method to automatically calculate the readings of pointer-type meters that exhibits strong robustness to various situations, such as complex backgrounds, tilted meters, blurred images, and meter images with uneven illumination. First, the mask maps of scale and pointers are obtained using the Swin-Unet semantic segmentation network. For the mask map of scale, the Swin Transformer image classification network is used to identify the values of the scale and the coordinates of the centroids of the scale, and for the mask map of the pointer, the least skeleton circle method is proposed to fit the linear equation of the pointer. Second, the influence of the pointer and several adjacent scale on the meter reading is considered, and the weighted angle method (WAM) is proposed to calculate the meter reading. In order to verify the robustness of the algorithm in this paper, the pointer detection method is compared with the traditional pointer detection method, and it is found that the pointer detection method in this paper works better, and the pointer detection algorithm in this paper is applied to different semantic segmentation results, and it is verified that the pointer detection algorithm in this paper can be well adapted to different semantic segmentation results. This paper also compares the proposed algorithm with the existing meter reading calculation methods. The experiments show that using WAM on uncorrected meter images reduces the error by 30% compared with the traditional angle method, and using WAM on corrected meter images reduces the error by about 50%, which finally verifies the effectiveness of the algorithm in this paper.

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID‐19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan‐Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far‐reaching problem in medical‐imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID‐19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine‐learning healthcare models.

Lumbar spine disorders represent a significant public health concern, with accurate diagnosis relying on vertebral segmentation and quantification. Traditional methods, such as Cobb angle measurement are constrained by two-dimensional projections, while cumulative segmentation and quantification errors limit automated CT analysis. To overcome these issues, this paper proposes a deep learning-based Boundary-Sensitive Network (BS-Net), integrating a Multi-Task Edge Processing (MEP) module and Contextual Bilateral Fusion (CBF) module to enhance vertebral edge feature extraction. The framework combines edge loss functions with morphological post-processing to achieve joint segmentation and quantification. Evaluations on 783 lumbar CT images from 379 patients and the public SPIDER MRI dataset demonstrate that BS-Net surpasses baseline models, achieving an MIoU of 96.56% and a Dice coefficient of 98.5%. Its spondylolisthesis quantification also shows strong agreement with manual assessment (ICC> 0.9). These results indicate that BS-Net provides an efficient and accurate solution for automated diagnosis of lumbar spondylolisthesis, with substantial clinical value.

With the rising incidence of degenerative lumbar spine disorders, accurate segmentation of spinal structures based on magnetic resonance imaging (MRI) is c