Improved VGG Algorithm for Visual Prosthesis Image Recognition

With the development of bio-informatics, visual prostheses have become an effective method for low vision people to restore visual function. To meet the visual needs of visual implant recipients, this study explored the problem of in vitro image processor image recognition and classification. It selected the convolutional neural network framework VGG as the technical core, introduced the fruit fly optimization algorithm for optimizing the VGG recognition model, and constructed a visual prosthesis image recognition model on the ground of improved VGG. The experiment demonstrated that the improved fruit fly search algorithm had an average absolute error and root mean square error values lower than 0.4, which was superior to other intelligent optimization algorithms. The performance of the improved image recognition model has been significantly improved, with a maximum AUC value of 0.942, a recognition accuracy range of 68.29%-97.23%, and a stable fitness curve of around 97.00. The maximum F1 value of the image recognition model designed in the study reached 91.47%, and the loss function curve converged to the minimum value. In the application of visual prostheses, the recognition accuracy and R-squared performance of this model were both the best. Compared with natural human vision, the contrast and functional visual effects matched well, the processing speed was faster, and the delay time did not affect the actual application of visual prostheses, achieving high user satisfaction. This study can enrich and improve the theoretical foundation of visual image analysis and visual prosthetics, and help visually impaired groups improve their lives and quality of life.