Research on Cyst of Jaw Detection Algorithm Based on Alex Net Deep Learning Model

Abstract

Abstract In clinical medicine, jawbone cysts are a common dental disease, and their symptoms are similar to other dental diseases, making diagnosis difficult. To address this issue, this paper proposes a cyst detection system based on Alex Net to achieve the detection of cysts on dental radiographs using a CBCT dataset. The system can detect potential cyst lesions and locations in a timely manner to assist doctors in diagnosis. The improved model achieves an average accuracy of 83.5% and a maximum accuracy of 99.9%, achieving a high cyst recognition rate on existing datasets. In addition, the extensive image enhancement techniques introduced in the Alex Net model also improve the performance of the model. The experimental results show that compared to Res Net and VGG Net, both networks are not ideal for the classification of jawbone cysts, and may not be able to effectively extract key features from medical images, resulting in low classification accuracy. Therefore, it is important to choose a suitable deep learning model for the diagnosis of specific dental diseases. In future research, it is possible to further explore how to combine multiple deep learning models to improve the accuracy of diagnosis of dental diseases such as jaw cysts. In addition, improving data preprocessing and enhancing techniques can further improve the generalization ability of the model. In summary, by combining deep learning and clinical medicine concepts and methods, more effective auxiliary diagnostic systems can be developed to improve the accuracy and efficiency of dental disease diagnosis.