The automatic segmentation of the lung region for chest X-ray (CXR) can help doctors diagnose many lung diseases. However, extreme lung shape changes and fuzzy lung regions caused by serious lung diseases may incorrectly make the automatic lung segmentation model. We improved the U-Net network by using the pre-training Efficientnet-b4 as the encoder and the Residual block and the LeakyReLU activation function in the decoder. The network can extract Lung field features efficiently and avoid the gradient instability caused by the multiplication effect in gradient backpropagation. Compared with the traditional U-Net model, our method improves about 2.5% dice coefficient and 6% Jaccard Index for the two benchmark lung segmentation datasets. Our model improves about 5% dice coefficient and 9% Jaccard Index for the private lung segmentation datasets compared with the traditional U-Net model. Comparative experiments show that our method can improve the accuracy of lung segmentation of CXR images and it has a lower standard deviation and good robustness.

Rice is a necessity for billions of people in the world, and rice disease control has been a major focus of research in the agricultural field. In this study, a new attention-enhanced DenseNet neural network model is proposed, which includes a lesion feature extractor by region of interest (ROI) extraction algorithm and a DenseNet classification model for accurate recognition of lesion feature extraction maps. It was found that the ROI extraction algorithm can highlight the lesion area of rice leaves, which makes the neural network classification model pay more attention to the lesion area. Compared with a single rice disease classification model, the classification model combined with the ROI extraction algorithm can improve the recognition accuracy of rice leaf disease identification, and the proposed model can achieve an accuracy of 96% for rice leaf disease identification.

Pneumothorax is a potentially life-threatening disease that requires urgent diagnosis and treatment. Clinically, a chest X-ray examination is the first choice for diagnosing pneumothorax. However, it is difficult to diagnose pneumothorax by only frontal chest X-ray imaging when the lesion area is only composed of a small amount of air. Therefore, we propose a pneumothorax diagnosis neural network based on feature fusion, where frontal and lateral X-ray information are fused. In this network, there are two inputs and three outputs. The two inputs are the frontal chest X-ray image and the lateral chest X-ray image. The three outputs are the classification results of the frontal chest X-ray image, the classification results of the lateral chest X-ray image, and the classification results integrating the characteristics of the fused frontal chest X-ray image and lateral chest X-ray image. Our algorithm considers the vanishing gradient problem in the pneumothorax recognition model and introduces the residual block to alleviate this problem. Because of the large number of channels in this model, we also utilize channel attention mechanisms to improve the model’s performance. Our comparative experiments show that neural network fusion of frontal and lateral chest image features can achieve higher accuracy than the single task model. Using only image-level annotation, our pneumothorax model can achieve high recognition accuracy.

Medical image segmentation is an essential process that facilitates the precise extraction and localization of diseased areas from medical pictures. It can provide clear and quantifiable information to support clinicians in making final decisions. However, due to the lack of explicit modeling of global relationships in CNNs, they are unable to fully use the long-range dependencies among several image locations. In this paper, we propose a novel model that can extract local and global semantic features from the images by utilizing CNN and the visual transformer in the encoder. It is important to note that the self-attention mechanism treats a 2D image as a 1D sequence of patches, which can potentially disrupt the image’s inherent 2D spatial structure. Therefore, we utilized the structure of the transformer using visual attention and large kernel attention, and we added a residual convolutional attention module (RCAM) and multi-scale fusion convolution (MFC) into the decoder. They can help the model better capture crucial features and fine details to improve detail and accuracy of segmentation effects. On the synapse multi-organ segmentation (Synapse) and the automated cardiac diagnostic challenge (ACDC) datasets, our model performed better than the previous models, demonstrating that it is more precise and robust in multi-organ medical image segmentation.

Automatic and highly accurate lung segmentation in chest X-ray (CXR) images is the basis of computer-aided diagnosis systems, because the lung is the region of interest of many diseases, and it can show useful information through its contours. However, automatic lung segmentation is immensely challenging due to extreme variations in the shape, obscure lung area, or opacity caused by lung diseases reaches high-intensity values. In the face of these severe situations, the model may segment the lung boundary incorrectly. We designed an improved U-Net network: using the pre-training Efficientnet-b4 as the encoder, and the residual block and LeakyRelu activation function are used in the decoder. The network can not only extract features with high efficiency but also avoid the gradient explosion caused by the multiplication effect in gradient backpropagation. We constructed a CXR lung field segmentation dataset (Haut) based on the NIH CXR dataset. In particular, this lung segmentation dataset contains some serious abnormal cases, such as lung deformation, pleural effusion, covered by foreign matters, or CXR blur caused by severe lung disease. The improved U-Net is evaluated on Haut, JSRT, and Montgomery County (MC) datasets. Experimental results show that our network can achieve high-precision lung segmentation.