Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network’s biomimetic performance of human attention. By incorporating matrices generated from shifted images into the self-attention mechanism, the network gains the ability to preemptively acquire information from surrounding regions. Experimental results demonstrate the superior performance of our approaches across various benchmark datasets, validating their effectiveness. Furthermore, the method was applied to patient kidney datasets collected from hospitals for diabetes diagnosis, where they achieved high accuracy with significantly reduced computational demands. This advancement showcases the potential of our methods in the field of biomimetics, aligning well with the goals of developing innovative bioinspired diagnostic tools.

The visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, including the Hassenstein–Reichardt model (HRC model) and the HRC-based artificial visual system (AVS). In this paper, we introduced a contrast-response system based on previous research on amacrine cells in the visual system of Drosophila and other species. We combined this system with the HRC-based AVS to construct a motion-direction-detection system for gray-scale images. Our experiments verified the effectiveness of our model in detecting the motion direction in gray-scale images, achieving at least 99% accuracy in all experiments and a remarkable 100% accuracy in several circumstances. Furthermore, we developed two convolutional neural networks (CNNs) for comparison to demonstrate the practicality of our model.