Deep learning-based image processing methods for medical brain tumors are current research hotspots in this field. However, a great deal of research has focused on how to classify and segment brain tumors, while relatively little research has been done on brain tumor detection. This is mainly because brain tumor images are often filled with indistinguishable lesions, which can easily lead to false positives or missed diagnoses, and the YOLO detection algorithm has attracted a lot of attention due to its efficient real-time target detection capability. Based on the YOLO framework, we created a new neural network to accurately identify lesion regions in brain tumor medical images. The core of this method is RGNet based on reparameterized large kernel heterogeneous convolution and a strategy GDB that combines an attention gathering-allocation mechanism. RGNet can better cope with significant changes in scale and different contextual feature textures in brain tumor detection, and GDB aggregates high-level and low-level semantic features and spatial details to overcome the information loss problem of the traditional feature pyramid module during feature fusion. information loss problem, and utilizes structured convolution module and channel mixing technique to improve the refinement of multi-dimensional details and enrich the semantic information during the whole feature fusion process. The experimental results show that compared with other object detection models, our model achieves 95.4% precision, 93.9% recall, 96.9% mAP50, and 74.8% mAP50:95 on the Br35H dataset ,and 47.1% precision, 86.1% recall, 54.0% mAP50, and 38.7% mAP50:95 on the Ultralytics brain tumor dataset. These numbers not only highlight the effectiveness of the proposed model in brain tumor detection, but also provide new ideas for the subsequent application of object detection in medical imaging and clinical disease diagnosis.

Medical image segmentation is widely used in clinical medicine, and the accuracy of the segmentation algorithm will affect the diagnosis results and treatment plans. However, manual segmentation of medical images requires extensive experience and knowledge, and it is both time-consuming and labor-intensive. To overcome the problems above, we propose a novel interval iterative multi-thresholding segmentation algorithm based on hybrid spatial filter and region growing for medical brain MR images. First, a hybrid spatial filter is designed to perform on the original image, which can make full use of the spatial information while denoising. Second, the interval iterative Otsu method based on region growing is proposed to segment the original image and its filtering layer. The initial thresholds can be quickly obtained by region growing algorithm, which can reduce the time complexity. The interval iterative algorithm is used to optimize the thresholds. Finally, a weighted strategy is used to refine the segmentation results. The segmentation results of our proposed algorithm outperform other comparison algorithms in both subjective and objective evaluations. Subjectively, the obtained segmentation results have clear edges, complete and consistent regions. We use the uniformity measure (U) for objective evaluation, and the U value is significantly higher than other comparison algorithms. The proposed algorithm achieved an average U value of 0.9854 across all test images. The proposed algorithm can segment medical images well and expand the doctor’s ability to utilize medical images.

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images.

Accurate identification of weld regions is a critical step in weld quality inspection and automated grinding operations. However, weld point cloud data is typically characterized by high irregularity and lack of topological structure, making its efficient processing and accurate recognition a persistent research challenge. In recent years, advanced machine learning methods have shown significant potential for weld identification, yet research on 3D vision-based recognition of three-dimensional weld point clouds remains in its early stages. This study focuses on three typical weld seam trajectories—linear, curved, and S-shaped—conducting classification and recognition experiments based on point cloud data. Using Overall Accuracy, Precision, Recall, and F1-score as evaluation metrics, a systematic comparison was performed on the classification performance of three models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—across different neighborhood scales. Experimental results indicate that LightGBM achieved optimal classification performance within a 1.5mm neighborhood range. To further enhance the classification accuracy, this study introduced the Artificial Lemming Algorithm (LAL), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA) to improve the LightGBM model. The optimized LAL-LightGBM, AE-LightGBM, and SFOA-LightGBM models attained accuracy rates on linear, curved, and S-shaped test sets of (98.02%, 96.07%, 93.87%), (98.83%, 96.36%, 94.18%), and (98.00%, 96.12%, 93.91%), respectively. Comprehensive results demonstrate that the AE-LightGBM model performed best in overall evaluation, verifying the effectiveness of intelligent optimization algorithms in improving classification model performance. Furthermore, this study employed the SHAP interpretability framework to analyze the feature contributions of the AE-LightGBM model, effectively revealing its decision-making mechanism and enhancing model interpretability. This research provides a feasible technical pathway for robot-based weld grinding recognition tasks utilizing 3D vision and supervised machine learning.