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Tooth Anatomy

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ACO-optimized MobileNetV2-ShuffleNet hybrid model for automated dental caries classification.

Kotturu Kaveri¹, Venkata Ratna Prabha K¹, G Pradeep Reddy²

¹Department of Electronics and Communication Engineering, Siddhartha Academy of Higher Education, Deemed to be University, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

Scientific Reports

|November 18, 2025

Summary

This summary is machine-generated.

This study introduces an automated method for classifying dental caries using panoramic X-rays. The novel approach enhances diagnostic accuracy, improving early detection of oral infections.

Keywords:

ACO Dental caries MobileNetV2 Panoramic radiographs ShuffleNet Sobel-Feldman edge detection

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Dental infections pose significant health risks if not promptly diagnosed.
Diagnosing oral infections from X-ray images is challenging due to subtle anatomical differences and data imbalance.
Accurate and timely diagnosis of dental caries is crucial for effective treatment.

Purpose of the Study:

To develop a robust and automated method for dental caries classification using panoramic radiographic images.
To address challenges of class imbalance and weak anatomical differences in dental X-rays.
To improve the accuracy and reliability of dental diagnosis through artificial intelligence.

Main Methods:

Preprocessing techniques including clustering for data balancing and Sobel-Feldman edge detection for feature enhancement.
Development of a hybrid deep learning architecture combining MobileNetV2 and ShuffleNet.
Integration of the Ant Colony Optimization (ACO) algorithm for parameter tuning and global search optimization.

Main Results:

Standalone MobileNetV2 and ShuffleNet models showed limited classification ability.
The hybrid architecture significantly improved classification precision.
The ACO-enhanced hybrid approach achieved a high accuracy of 92.67%, outperforming individual networks.

Conclusions:

The proposed ACO-enhanced hybrid model demonstrates superior performance for dental caries classification.
This automated approach offers a reliable tool for dentists, enhancing diagnostic efficiency.
The method holds potential for widespread application in automated dental diagnosis systems.