Skin disease is one of the most common diseases. Due to the intricate categories of skin diseases, their symptoms being very similar in the early stage, and the lesion samples being extremely unbalanced, their classification is challenging. At the same time, under the conditions of limited data, the generalization ability of a single reliable convolutional neural network model is weak, the feature extraction ability is insufficient, and the classification accuracy is low. Therefore, in this paper, we proposed a convolutional neural network model for skin disease classification based on model fusion. Through model fusion, deep and shallow feature fusion, and the introduction of an attention module, the feature extraction capacity of the model was strengthened. In addition, a series of works such as model pre-training, data augmentation, and parameter fine-tuning were conducted to upgrade the classification performance of the model. The experimental results showed that when working on our private dataset dominated by acne-like skin diseases, our proposed model outperformed the two baseline models of DenseNet201 and ConvNeXt_L by 4.42% and 3.66%, respectively. On the public HAM10000 dataset, the accuracy and f1-score of the proposed model were 95.29% and 89.99%, respectively, which also achieved good results compared with other state-of-the-art models.

Research on lung cancer automatic detection using deep learning algorithms has achieved good results but, due to the complexity of tumor edge features and possible changes in tumor positions, it is still a great challenge to diagnose patients with lung tumors based on computed tomography (CT) images. In order to solve the problem of scales and meet the requirements of real-time detection, an efficient one-stage model for automatic lung tumor detection in CT Images, called ELCT-YOLO, is presented in this paper. Instead of deepening the backbone or relying on a complex feature fusion network, ELCT-YOLO uses a specially designed neck structure, which is suitable to enhance the multi-scale representation ability of the entire feature layer. At the same time, in order to solve the problem of lacking a receptive field after decoupling, the proposed model uses a novel Cascaded Refinement Scheme (CRS), composed of two different types of receptive field enhancement modules (RFEMs), which enables expanding the effective receptive field and aggregate multi-scale context information, thus improving the tumor detection performance of the model. The experimental results show that the proposed ELCT-YOLO model has strong ability in expressing multi-scale information and good robustness in detecting lung tumors of various sizes.

With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO (Efficient-Fast-Shared-YOLO) model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model’s parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.

Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized complex modules to improve their performance for skin disease image segmentation. However, limited computational resources have hindered their practical application in clinical environments. To address this challenge, this paper proposes a lightweight model, named GSCEU-Net, which is able to achieve superior skin lesion segmentation performance at a lower cost. GSCEU-Net is based on the U-Net architecture with additional enhancements. Firstly, the partial convolution (PConv) module, proposed by the FasterNet model, is modified to an SConv module, which enables channel segmentation paths of different scales. Secondly, a newly designed Ghost SConv (GSC) module is proposed for incorporation into the model’s backbone, where the Separate Convolution (SConv) module is aided by a Multi-Layer Perceptron (MLP) and the output path residuals from the Ghost module. Finally, the Efficient Channel Attention (ECA) mechanism is incorporated at different levels into the decoding part of the model. The segmentation performance of the proposed model is evaluated on two public datasets (ISIC2018 and PH2) and a private dataset. Compared to U-Net, the proposed model achieves an IoU improvement of 0.0261 points and a DSC improvement of 0.0164 points, while reducing the parameter count by 190 times and the computational complexity by 170 times. Compared to other existing segmentation models, the proposed GSCEU-Net model also demonstrates superiority, along with an advanced balance between the number of parameters, complexity, and segmentation performance.

Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management.