Impact of Selective median filter on dental caries classification system using deep learning models

Abstract

<p>Accurate classification of dental caries is crucial for effective oral healthcare. Filters help to increase exposure of the picture taken for the investigation without degrading image quality. Selective median filter is the chosen preprocessing technique that helps to reduce the noise present in the captured image. Dental caries classification system is a model used to detect the presence of cavity in the given input image. Dental caries classification system is evolved with the use of conventional techniques to artificial neural network. Deep learning models are the artificial neural network models that can able to learn the features from the raw images available in the dataset. If this raw image has noise, then it severely affects the accuracy of the deep learning models. In this paper, impact of the preprocessing technique on the classification accuracy is analyzed. Initially, raw images are taken for training on deep learning models without applying any preprocessing technique. This study investigates the impact of Selective median filtering on a dental caries classification system using deep learning models. The motivation behind this research is to enhance the accuracy and reliability of dental caries diagnosis by reducing noise, removing artifacts, and preserving important details in dental radiographs. Experimental results demonstrate that the implementation of Selective median filtering significantly improves the performance of the deep learning model. The hybrid neural network (HNN) classifier achieves an accuracy of 96.15% with Selective median filtering, outperforming the accuracy of 85.07% without preprocessing. The study highlights the theoretical contribution of Selective median filtering in enhancing dental caries classification systems and emphasizes the practical implications for dental clinics, offering improved diagnostic capabilities and better patient outcomes.</p>

Industry 4.0 Transformation: Adaptive Coati Deep Convolutional Neural Network-based Oral Cancer Diagnosis from Histopathological Images

“…Zero padding method is used in this layer to preserve the size of the input features, while the RLU is utilized as the non-linear activation function. The RLU layer retains positive values and sets negative values to zero using equation (15).…”

“…Deep learning may be employed to evaluate OSCC photos and find features that are specific to OSCC in the case of OSCC detection [12][13][14]. Deep learning algorithms have the benefit of being trained on big datasets, which can increase the model's accuracy [15,16]. Large datasets of OSCC photos are now accessible for OSCC identification, and these datasets can be utilised to train deep learning models.…”