Gender Classification Using AI and Dental X-Rays : Advancements in Forensic Identification
AI Tool to Identify Gender Using Teeth X-Ray in 2024 : In the aftermath of large-scale disasters, one of the primary challenges faced by forensic experts is the identification of missing persons or deceased individuals. Traditional methods of identification often rely on various biological markers, but teeth have proven to be one of the most reliable due to their durability. Recent advancements in artificial intelligence (AI) have paved the way for innovative methods in forensic identification, particularly through the use of dental X-rays. This blog explores the development and efficacy of a new AI-powered gender classification system using deep convolutional neural networks (CNN) on digital dental X-ray images (DXI).
Importance of Dental X-Rays in Forensics
Human teeth are remarkably resilient, often remaining intact even when other parts of the body have decomposed. This resilience makes teeth an invaluable tool in forensic science for identifying unknown individuals. The ability to determine gender from dental X-rays not only aids in identification but also narrows down the pool of potential matches, making the identification process more efficient.
Advantages of the AI Tool
The AI tool to identify gender using teeth X-rays in 2024 offers several advantages over traditional methods:
- High Accuracy: With a classification accuracy of 98.27%, this AI tool outperforms many existing methods.
- Efficiency: The model is trained quickly without requiring extensive computational resources, making it practical for real-world applications.
- Automation: The automated process reduces the risk of human error and speeds up the identification process in forensic investigations.
The Proposed AI System
Objectives
The primary goal of this research was to develop an automated gender estimation model using a deep convolutional neural network. The system is designed to provide high accuracy in gender classification, thereby supporting forensic experts in their identification tasks.
Methodology of AI and Dental X-Rays
The AI X-ray system to identify gender and age involves three key steps:
- Pre-processing: The DXI is denoised to remove unwanted noise such as Gaussian, speckle, and impulse using a prime magic square filter.
- Segmentation: A gradient-based recursive thresholding algorithm is applied to segment the denoised DXI, focusing on the mandibular region, which remains largely unaffected by decomposition.
- Classification: The segmented image is classified using the ResNet50 deep convolutional neural network, a powerful model known for its efficiency and accuracy in image classification tasks.
Performance and Accuracy
The system was trained on a dataset of 1000 dental images, with 600 used for training and 400 for testing. The proposed method achieved a remarkable 98.27% accuracy in gender classification, outperforming existing methods. This high level of accuracy demonstrates the potential of AI X-ray systems to identify gender and age in forensic applications, particularly in scenarios involving mass disasters.
The Proposed Method: A Deep Learning Approach
The proposed method for gender classification involves three key steps: pre-processing, segmentation, and classification. Initially, the dental X-ray images are denoised using a prime magic square filter to remove unwanted noise. The next step involves segmenting the denoised images using a gradient-based recursive thresholding algorithm. Finally, the Resnet50 deep convolutional neural network classifies the images to determine gender. This AI X-ray to identify gender and age process is both efficient and accurate.
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Comparative Analysis of AI tooth X-ray gender identification
Previous Research
Previous studies, such as the one conducted by Tangel et al., utilized various image processing techniques but achieved lower accuracy rates. The integration of AI X-ray systems to identify gender and disease represents a significant advancement in this field.
New AI System
The proposed AI X-ray system to identify gender check utilizes advanced deep learning techniques, specifically the ResNet50 architecture. This model has proven to be highly effective in handling noisy datasets and complex image classifications, making it an ideal choice for forensic applications.
Experimental Results
The experimental results were implemented using MATLAB 2020a software. The ResNet50 model parameters were fine-tuned to optimize performance, resulting in a highly accurate classification system. The AI X-ray system to identify gender and age has shown to be reliable across various image resolutions and age groups, although accuracy can vary slightly with lower image quality and younger individuals.
Experimental Evaluation and Results
The system was evaluated using a database of 1000 dental images, divided into 600 training samples and 400 testing samples. The proposed AI X-ray to identify gender and disease achieved an impressive 98.27% accuracy in gender classification. This high accuracy underscores the effectiveness of the AI-driven approach compared to existing methods.
Future Work and Conclusion
The success of the proposed AI X-ray system to identify gender and disease opens up new avenues for research and application in forensic science. Future work could focus on expanding the dataset, improving image resolution, and further refining the model to handle more complex cases involving younger individuals and lower-quality images.
In conclusion, the integration of AI in dental X-ray analysis represents a significant step forward in forensic identification. With an accuracy rate of 98.27%, the proposed system demonstrates the potential of AI X-ray systems to identify gender and age with high precision, providing invaluable support in forensic investigations and mass disaster scenarios.
This blog highlights the advancements in AI-powered gender classification using dental X-rays, showcasing the potential of this technology in forensic science.
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