• G. KRISHNAVENI
• G. SRINIVASA RAO
• V. SIVA PARVATHI
• U. LAKSHMI TULASI
• M. SUJATHA

Dental image processing has gained significant attention in recent years due to its potential in automating the detection of dental conditions such as cavities and aiding in treatment planning. This study focuses on utilizing advanced deep learning models, specifically Convolutional Neural Networks (CNNs), VGG16, and ResNet, to analyze dental images for cavity detection and restoration planning. The research proposes an end-to-end framework that uses these architectures for automatic cavity detection from dental X-rays. The system is trained on a large dataset of dental radiographs to learn patterns indicative of cavities, including early-stage decay and more advanced lesions. CNNs, known for their powerful feature extraction capabilities, are employed to capture complex spatial patterns in dental images, enhancing the accuracy of detection. To improve model performance and accuracy, the study explores the application of pre-trained models such as VGG16 and ResNet. VGG16, with its deep layers, allows for a detailed feature extraction process, while ResNet’s residual learning architecture helps in mitigating the vanishing gradient problem, thus ensuring deeper and more accurate network training. These models are fine-tuned to adapt to dental-specific features. The results demonstrate the feasibility and effectiveness of using deep learning techniques for cavity detection, showing that CNN, VGG16, and ResNet can significantly reduce diagnostic time while improving accuracy. Additionally, the system assists in planning restorations by providing insights into cavity size, depth, and location, allowing for more informed treatment decisions. In conclusion, this study highlights the potential of deep learning in the field of dental image processing, particularly for automatic cavity detection and restoration planning. Future work could involve integrating this approach into clinical practice for real-time diagnostics and personalized treatment planning.

Dental Image Processing, Deep Learning, Convolutional Neural Networks (CNNs), VGG16, ResNet, Cavity Detection, Automatic Diagnosis, Dental Radiographs, Medical Image Analysis, Real-time Diagnostics.

"DENTAL IMAGE PROCESSING FOR CAVITY DETECTION AND RESTORATION PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n494-n498, April-2025, Available :http://www.jetir.org/papers/JETIR2504D58.pdf

"DENTAL IMAGE PROCESSING FOR CAVITY DETECTION AND RESTORATION PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn494-n498, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D58.pdf