September 2025 - 38 articles
Early detection of breast cancer via mammography enhances patient survival rates, prompting this study to assess object detection models—Cascade R-CNN, YOLOv12 (S, L, and X variants), RTMDet-X, and RT-DETR-X—for detecting masses and calci...
Obesity is linked to suboptimal breastfeeding outcomes, yet the relationships between maternal adiposity, breast anatomy, and milk production (MP) have not been investigated. We conducted ultrasound imaging to assess the breast anatomy of 34 lactatin...
Currently, deepfake detection has garnered widespread attention as a key defense mechanism against the misuse of deepfake technology. However, existing deepfake detection networks still face challenges such as insufficient robustness, limited general...
This study introduces a novel enhancement to the UNet architecture, termed Cascaded Spatial and Depth Attention U-Net (CSDA-UNet), tailored specifically for precise hippocampus segmentation in T1-weighted brain MRI scans. The proposed architecture in...
This study aimed to evaluate the reliability of an AI-based software tool in measuring spinal parameters—Cobb angle, thoracic kyphosis, lumbar lordosis, and pelvic obliquity—compared to manual measurements by radiologists and to assess po...
This study aimed to develop a local dataset of abnormal RBC morphology from confirmed cases of anemia and thalassemia in Thailand, providing a foundation for medical image analysis and future AI-assisted diagnostics. Blood smear samples from six hema...
The large-scale evaluation of multiple-choice tests is a challenging task from the perspective of image processing. A typical instrument is a multiple-choice question test that employs an answer sheet with circles or squares. Once students have finis...
Image quality assessment (IQA) aims to measure the consistency between an objective algorithm output and a subjective perception measurement. This article focuses on this complex relationship in the context of color image scenarios—color image...
Retinal vessel segmentation is critical for early diagnosis of diabetic retinopathy, yet existing deep models often compromise accuracy for complexity. We propose DSAE-Net, a lightweight dual-stage network that addresses this challenge by (1) introdu...
Fish diseases are one of the primary causes of economic losses in aquaculture. Existing deep learning models have progressed in fish disease detection and lesion segmentation. However, many models still have limitations, such as detecting only a sing...
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