Diabetic retinopathy (DR) is a microvascular complication of diabetes. Microaneurysms (MAs) are often observed in the retinal vessels of diabetic patients and represent one of the earliest signs of DR. Accurate and efficient detection of MAs is crucial for the diagnosis of DR. In this study, an automatic model (MA-YOLO) is proposed for MA detection in fluorescein angiography (FFA) images. To obtain detailed features and improve the discriminability of MAs in FFA images, SwinIR was utilized to reconstruct super-resolution images. To solve the problems of missed detection of small features and feature information loss, an MA detection layer was added between the neck and the head sections of YOLOv8. To enhance the generalization ability of the MA-YOLO model, transfer learning was conducted between high-resolution images and low-resolution images. To avoid excessive penalization due to geometric factors and address sample distribution imbalance, the loss function was optimized by taking the Wise-IoU loss as a bounding box regression loss. The performance of the MA-YOLO model in MA detection was compared with that of other state-of-the-art models, including SSD, RetinaNet, YOLOv5, YOLOX, and YOLOv7. The results showed that the MA-YOLO model had the best performance in MA detection, as shown by its optimal metrics, including recall, precision, F1 score, and AP, which were 88.23%, 97.98%, 92.85%, and 94.62%, respectively. Collectively, the proposed MA-YOLO model is suitable for the automatic detection of MAs in FFA images, which can assist ophthalmologists in the diagnosis of the progression of DR.

Segmentation of island waterline contributes to shoreline movement analyzing, environmental monitoring, and integrated coastal zone management. To achieve high efficiency and high accuracy of island waterline segmentation in remote sensing images, we proposed a model for island waterline segmentation based on DANet (DANet-SMIW). In DANet-SMIW model, different indexes (Normalized Difference Water Index and OTSU) were taken as new channels added to input dataset, which enhanced waterline's spectral information. DANet backbone network was improved by dense connection of DenseNet. Loss function, consisting of binary cross entropy loss and Dice loss, was used to resolve the sample imbalance problem, and then, rough results of island waterline segmentation were refined by boundary refinement module (BRM). In total, 2042 island images were taken as experiment dataset, which were cropped from Landsat-8 images and divided into 1634 images for training, 100 images for testing, and 308 images for validation, and DANet-SMIW model was compared against other models, including FCN-32s, DeepLabv3+, PSPNet, Dense-ASPP, PSANet, ICNet, DuNet, and PIDNet. Results demonstrated that DANet-SMIW model achieved the highest values with pixel accuracy and Mean Intersection over Union and possessed higher segmentation efficiency than most other models. Collectively, DANet-SMIW model was an integrated accurate and efficient model for island waterline segmentation in remote sensing images.

As individuals age, the influence of lifestyle on health becomes increasingly evident. Notably, alcohol consumption has drawn particular attention due to its potential role in the development of age-related diseases [1]. Targeted intervention in later life addressing this factor could improve overall public health outcomes. Age-related macular degeneration (AMD) is a leading cause of vision […]