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This article discusses the use of convolutional neural networks (CNNs) for the automatic detection of dental apical lesions in periapical radiographs, addressing the limitations of manual marking which is time-consuming and prone to variability. The proposed CNN model achieved a diagnostic accuracy of 92.5% by utilizing a standardized image database and advanced image preprocessing techniques. This approach aims to enhance the efficiency of dental diagnoses, allowing practitioners to focus more on treatment

Author

Ch Bilal

Language

EN