RETRACTED ARTICLE: Application of new optical imaging equipment based on deep learning in kidney tumor image segmentation and recognition

The precise classification of kidneys and kidney tumors is of great guiding significance for the diagnosis and surgical planning of clinical doctors. Artificial segmentation of kidneys and renal tumors not only increases the workload of doctors, but also has a certain degree of subjectivity. Due to the varying morphology and pathological characteristics of various kidney tumors, conventional imaging analysis methods have certain limitations in their application. This study aims to propose a novel optical imaging device based on deep learning to achieve better results in kidney tumor image segmentation and recognition. The study collected a batch of image data on kidney tumors and trained the data using deep learning algorithms. After many experiments and optimization, a model with excellent performance in renal tumor segmentation and recognition was obtained. Compared with several existing methods, the method proposed in this paper has better segmentation accuracy for kidney tumors. This can not only avoid mutual interference between the network and the resolution network, but also improve the recognition efficiency between the two networks. In addition, the system can also provide reliable data feedback for clinical doctors, helping them continuously correct problems in subsequent visual examinations, thereby achieving higher diagnostic accuracy. The experimental results show that the neural network is effective in image segmentation and quantitative analysis, and can be applied in clinical practice.