Segmentation of blood vessels in retinal fundus images is crucial for detecting and diagnosing eye diseases. In recent years, the application of deep learning methods based on convolutional neural networks (CNN) in retinal blood vessel segmentation has made significant progress in performance. However, the down-sampling operation used by CNN to extract high-level contextual semantics will cause the loss of low-level blood vessel spatial details. Therefore, accurately segmenting retinal blood vessels remains challenging. In this paper, we propose a hierarchical full-resolution fusion network (HFRF-Net) for accurate blood vessel segmentation from retinal fundus images. In HFRF-Net, we design spatial path and contextual path based on a hierarchical structure, which is used to extract rich spatial features and sufficient contextual semantics respectively. In the contextual path, we propose a cross-scale attention (CSA) block to simulate the interaction at multiple scales in the vascular region. Then, to cope with the loss of spatial information, we propose a full-resolution learning strategy, which consists of a spatial detail enhancement (SDE) block and a full-resolution fusion (FRF) block. The SDE block performs fine-grained analysis and feature extraction of blood vessels by multiplexing the original fundus images in the spatial path. Meanwhile, the FRF block is proposed to guide the full-resolution information fusion of contextual path and spatial path, which preserves the microstructure of blood vessels to the greatest extent. In addition to the design of the network architecture, we also propose a topology-aware connectivity booster (TCB) to improve the connectivity of blood vessels in the segmentation map. We conducted extensive experiments on three mainstream retinal fundus image datasets (DRIVE, CHASE-DB1, and STARE). Compared to other state-of-the-art methods, the results demonstrate that our method achieves superior segmentation performance.

In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition.