ACO-optimized MobileNetV2-ShuffleNet hybrid model for automated dental caries classification
Revolutionizing Dental Diagnostics with AI
Dental infections, if not diagnosed promptly, can lead to severe health issues. This paper proposes a novel method for dental caries classification using panoramic radiographic images, addressing class imbalance and subtle anatomical differences. The preprocessing stage employs clustering to balance data distribution and Sobel-Feldman edge detection to highlight critical features. While standalone MobileNetV2 and ShuffleNet models showed poor classification, a hybrid architecture combining their strengths was designed, significantly improving precision. To further enhance performance, the Ant Colony Optimization (ACO) algorithm was integrated into the hybrid framework for efficient global search and parameter tuning. The ACO-enhanced hybrid approach achieved 92.67% accuracy, outperforming standalone networks, making it a reliable tool for automated dental diagnosis.
Executive Impact: Key Performance Indicators
Our ACO-optimized hybrid model delivers unparalleled accuracy and efficiency, setting new benchmarks for automated dental caries classification. These key metrics demonstrate the significant advancements and robust performance achieved.
0%
Accuracy
0%
Precision
0%
Recall
0
AUC Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The proposed method leverages a hybrid architecture combining MobileNetV2 and ShuffleNet. MobileNetV2 excels at extracting features using depthwise separable convolutions, identifying gross patterns like edges and textures efficiently. ShuffleNet, inspired by group convolutions and channel shuffling, optimizes for small networks to encode rich representation information, crucial for handling many X-ray images within a short time. This combination ensures a robust feature extraction backbone suitable for complex dental imaging.
Ant Colony Optimization (ACO) is a nature-inspired metaheuristic used to fine-tune the hyperparameters of the hybrid model. ACO algorithms, based on the foraging behavior of ants, excel at global search and parameter optimization. By using pheromone trails and heuristic data, ACO probabilistically guides the search for optimal solutions, iteratively refining learning rate, momentum, and batch size to maximize validation accuracy and minimize overfitting. This optimization step significantly enhances the model's overall performance and convergence.
Effective preprocessing techniques are vital for improving model understanding and performance. The method utilizes clustering to address class imbalance in the dataset, ensuring a balanced distribution of caries and non-caries images. Subsequently, the Sobel-Feldman edge detection technique is applied. This operator identifies image gradients, highlighting crucial features like teeth edges, caries regions, and other relevant structures in X-ray images, thereby aiding in segmentation and classification and making the images more understandable for the model.
92.67%
Achieved Accuracy with ACO-Enhanced Hybrid Model
Dental Caries Classification Workflow
Input X-ray Images
→
Clustering for Balanced Dataset
→
Sobel-Feldman Preprocessing
→
Hybrid MobileNetV2-ShuffleNet Feature Extraction
→
ACO for Hyperparameter Optimization
→
Caries / Normal Classification Output
| Model | Accuracy | Key Features |
|---|---|---|
| MobileNetV2 (Standalone) | 76.60% |
|
| ShuffleNet (Standalone) | 82.89% |
|
| Proposed Hybrid Model (without ACO) | 83.55% |
|
| Proposed Hybrid Model (with ACO) | 92.67% |
|
Impact of ACO Optimization
The integration of the Ant Colony Optimization (ACO) algorithm proved crucial in boosting the model's performance from 83.55% accuracy to 92.67%. ACO's ability to perform an efficient global search and fine-tune parameters like learning rate, momentum, and batch size significantly improved the model's convergence and accuracy. This highlights the importance of intelligent optimization strategies in deep learning for medical diagnosis, especially in overcoming challenges like class imbalance and subtle feature differences.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing AI-powered solutions.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your enterprise AI solutions.
Phase 1: Discovery & Strategy
Initial consultation, needs assessment, data audit, and strategic planning for AI integration within your specific dental diagnostic workflows.
Phase 2: Custom Model Development
Tailoring the hybrid MobileNetV2-ShuffleNet model and ACO optimization to your specific data, ensuring optimal performance and compliance.
Phase 3: Integration & Testing
Integrating the AI solution with existing systems, rigorous testing, and validation with real-world panoramic radiographs to ensure accuracy and reliability.
Phase 4: Deployment & Optimization
Full deployment, ongoing monitoring, performance optimization, and continuous improvement based on new data and operational feedback.
Ready to Transform Your Dental Diagnostics?
Join leading healthcare providers leveraging advanced AI for precise, automated dental caries classification. Schedule a consultation to explore how our solution can benefit your practice.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat