DS-MENet for the classification of citrus disease

Segmentation detection method in tree-shaded environment for road cracks collected by inspection vehicle on WFU-Unet

Road cracks pose a significant safety hazard to transportation, making timely detection crucial for traffic safety. Traditional crack segmentation methods face three main issues: (1) Tree shadow background affects crack recognition in real-world environments. (2) Conventional convolutional neural networks fail to detect complete cracks. (3) Direct deconvolution during upsampling results in unclear crack details. To address these challenges, this paper proposes the WFU-Unet model for road crack detection and segmentation. First, the WCM module, constructed with wavelet transform, ConvNext, and MobileNet, reduces shadow interference, enabling the network to distinguish between cracks and tree shadows. Second, the Fuse module replaces traditional convolutional blocks, enhancing the network's ability to extract crack features. Finally, the Up module substitutes conventional upsampling techniques to minimize spatial information loss of cracks. Experimental results show that the WFU-Unet model achieves a Miou of 81.68%, precision of 91.43%, recall of 86.63%, and F1-Score of 88.93%. Compared to other models, WFU-Unet demonstrates superior generalization ability and segmentation accuracy, making it more suitable for crack detection in shadowed environments.

A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet

Accurate segmentation of the stem of pumpkin seedlings has a great influence on the modernization of pumpkin cultivation, and can provide detailed data support for the growth of pumpkin plants. We collected and constructed a pumpkin seedling point cloud dataset for the first time. Potting soil and wall background in point cloud data often interfere with the accuracy of partial cutting of pumpkin seedling stems. The stem shape of pumpkin seedlings varies due to other environmental factors during the growing stage. The stem of the pumpkin seedling is closely connected with the potting soil and leaves, and the boundary of the stem is easily blurred. These problems bring challenges to the accurate segmentation of pumpkin seedling point cloud stems. In this paper, an accurate segmentation algorithm for pumpkin seedling point cloud stems based on CPHNet is proposed. First, a channel residual attention multilayer perceptron (CRA-MLP) module is proposed, which suppresses background interference such as soil. Second, a position-enhanced self-attention (PESA) mechanism is proposed, enabling the model to adapt to diverse morphologies of pumpkin seedling point cloud data stems. Finally, a hybrid loss function of cross entropy loss and dice loss (HCE-Dice Loss) is proposed to address the issue of fuzzy stem boundaries. The experimental results show that CPHNet achieves a 90.4% average cross-to-merge ratio (mIoU), 93.1% average accuracy (mP), 95.6% average recall rate (mR), 94.4% F1 score (mF1) and 0.03 plants/second (speed) on the self-built dataset. Compared with other popular segmentation models, this model is more accurate and stable for cutting the stem part of the pumpkin seedling point cloud.

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method

BACKGROUND

Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis.

PURPOSE

This study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis.

METHODS

We constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result.

RESULTS

In this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%.

CONCLUSION

The accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.