Design and Analysis of Abnormally Positioned Teeth Detection Using Advanced Representation Learning Based Feature Engineering with Optimization Algorithm on Dental X-Ray

Abstract

Dental radiographs are widely used in clinical diagnosis for identifying abnormalities in tooth position such as crowding, impacted teeth, rotated teeth, and malocclusion. Manual analysis of dental X-rays is time-consuming and depends heavily on the expertise of dentists. This paper proposes an automated framework for detecting abnormally positioned teeth from dental X-ray images using advanced representation learningbased feature engineering combined with an optimization algorithm. The proposed approach includes preprocessing, tooth region segmentation, deep feature extraction using representation learning models, feature selection/engineering, and classification. To enhance performance, an optimization algorithm is applied for selecting optimal features and tuning hyperparameters. The experimental results demonstrate improved accuracy, precision, and recall compared to traditional machine learning and basic CNN models. This system supports dentists in early detection, treatment planning, and improving diagnostic efficiency.

Introduction

Dental radiography is essential for diagnosing and planning orthodontic treatments, helping detect issues like misalignment, crowding, and impacted teeth. Manual analysis of X-rays, while effective, is time-consuming and depends on expert knowledge, and can be challenging due to poor image quality and overlapping structures.

To address these limitations, the paper proposes an automated system using machine learning and deep learning techniques. While models like CNNs and transfer learning improve feature extraction, they may suffer from overfitting and irrelevant features. Therefore, a hybrid approach is introduced, combining deep feature extraction, feature engineering, and optimization algorithms to enhance accuracy and robustness.

The system is designed as a multi-stage pipeline: input X-ray → preprocessing → segmentation → feature extraction → feature selection → optimization → classification. It classifies teeth alignment as normal or abnormal and supports early diagnosis for better clinical decision-making.

The study builds on previous research in image processing, machine learning, and deep learning, improving upon them by integrating optimization techniques. The implementation uses Python with libraries like OpenCV, TensorFlow, and Scikit-learn, and leverages GPU support for efficient model training.

Conclusion

This paper presented a framework for detecting abnormally positioned teeth using dental X-ray images. The proposed approach integrates representation learning-based feature extraction, feature engineering, and optimization algorithms to enhance detection accuracy. The system improves performance compared to basic CNN and traditional machine learning approaches by selecting optimal features and reducing redundant information. Experimental analysis shows that the model achieves high accuracy, precision, and recall, making it suitable for assisting dentists in early diagnosis and orthodontic treatment planning. The proposed approach provides a reliable and scalable solution for automated dental abnormality detection. [1] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” IEEE Transactions on Medical Imaging, vol. 42, no. 1, pp. 60–88, Jan. 2017.

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Copyright

Copyright © 2026 Mrs. Sri Kalaivani R., Mr. Rajesh Kumar S.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.