Redefining dental image processing: De-convolutional component with residual prolonged bypass for enhanced teeth segmentation

Dental diseases have risen in the past few years due to improper hygiene. Early detection and diagnosis can control this rapid growth in dental diseases. Therefore, different traditional techniques are employed for the detection of dental problems. However, these classical techniques such as X-Ray and CT scans are considered to be time-consuming, ineffective, and prone to errors due to human intervention. Hence, AI techniques are used to obtaining precise outcomes for dental-related issues. The conventional ML (Machine Learning) techniques are inefficient for obtaining enhanced outcomes as the efficiency of ML techniques heavily depends on image processing approaches. They are performed and also the quality of the features that have been extracted. Further, ML techniques lack in producing better outcomes while dealing with huge datasets. Therefore, the proposed model employs DL (Deep Learning) techniques due to its capability to learn the features strongly from the data by using a general-purpose learning procedure. So, DL techniques can work efficiently on huge datasets. The proposed DC (De-convolution Component) with RES (Residual Prolonged Bypass) is employed in the present research work as it is responsible to increase the spatial resolution of the feature maps and helps in recovering lost spatial information during the down sampling process. Likewise, the RES model aids in proficiently proliferating both low-level and high-level features to the deep layers, which help in generating better-segmented images. RES model includes prolonged bypass paths that carry feature information across multiple layers. This ensures that features extracted at earlier layers (low-level features) are available at much deeper layers. Implementation of the present research work contributes to enhancing the overall performance and effectiveness in detecting and diagnosing various dental issues and possesses the capability to work on both small and massive datasets effectively. Also, the proposed work contributes to deliver better accuracy, IoU (Intersection Over Union) and Dice coefficient, compared to Multi-Headed CNN and Context Encoder-Net, thereby assisting dental professionals in the detection and diagnosis of various dental issues due to the effectiveness of the proposed model.