Dental image segmentation for carrier detection using improved MLP-UNet model in dental X-ray images

Dental X-ray picture segmentation assists tooth diagnosis. Too much chocolate and unhealthy diets have increased tooth diseases in recent decades. Dental radiography supports clinical diagnosis, treatment, and quality assessment. Clinical quality has been improved by digitalising dental X-ray image analysis systems. This inspires an early-detection dental disease prediction model. Dental X-ray image segmentation for disease diagnosis is gaining attention. Deep learning has grown in various image processing domains. Image segmentation is a key field of computer vision research. The U-Net, a popular picture segmentation method, has been widely applied to medicine. Here, a unique deep learning system, the UNet model improved with ANN, notably MLP, segments dental pictures better. An ANN-enhanced U-Net split the X-ray pictures, and a GLCM selected the features. CNN-mobile net classifier. Four metrics - global accuracy, mean accuracy, mean IoU, weighted IoU, and MeanBFScore - and classification with precision, recall, Fscore, specificity, and accuracy evaluate segmentation methodology. Our method is accurate and outperforms others in all segmentation and classification tasks.