The automated process for dental caries detection draws increasing attention with the technological innovation in machine learning methods. This is a core issue in dental diseases especially in the detection of caries as it leads to serious health ailments. This paper takes an effort to adequately segment and identify dental diseases. There are four main steps. The preprocessing technique uses binary histogram equalization which increases the texture region visibility for the caries detected on dental images. The novel technique of segmentation with Curvilinear Semantic Deep Convolutional Neural Network (CSDCNN) is proposed in this paper . The segmentation is followed by the proposed Inception resnetV2, which acts as the classification technique to determine the caries in dental images. The proposed segmentation algorithm is used to determine a dental degree of membership. The inception is brought out with different scales of information, which relates to various input images as data. An examination of the x-ray images will detect the impact of illness on a tooth. Particularly for the segmentation and classification mission, we deemed four diseases: dental caries, periapical infection, periodontal, and pericoronal diseases. Based on the number of input functional parameters, the Inception resnetV2 classifies different image categories effectively. The proposed Inception resnetV2 has become the most effective tool in machine learning to solve problems like image classification with a high order of accuracy. The average accuracy of the device proposed is 94.51%. This provides higher classification accuracy when compared to other existing methods.

随着机器学习方法的技术创新，龋齿检测的自动化过程越来越受到关注。这是牙科疾病的核心问题，特别是在龋齿检测方面，因为它会导致严重的健康疾病。本文努力充分细分和识别牙科疾病。有四个主要步骤。预处理技术使用二元直方图均衡化，增加了牙科图像上检测到的龋齿的纹理区域可见性。本文提出了曲线语义深度卷积神经网络（CSDCNN）分割的新技术。分割之后是提议的 Inception resnetV2，它充当确定牙科图像中的龋齿的分类技术。所提出的分割算法用于确定牙科隶属度。初始是通过不同尺度的信息产生的，这些信息与作为数据的各种输入图像相关。X 射线图像检查将检测疾病对牙齿的影响。特别是对于分割和分类任务，我们认为有四种疾病：龋齿、根尖周感染、牙周病和冠周疾病。Inception resnetV2根据输入功能参数的数量，有效地对不同的图像类别进行分类。所提出的 Inception resnetV2 已成为机器学习中最有效的工具，可以高精度地解决图像分类等问题。所提出设备的平均准确率为 94.51%。