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A Random Forest-based Automatic Classification of Dental Types and Pathologies using Panoramic Radiography Images

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Title A Random Forest-based Automatic Classification of Dental Types and Pathologies using Panoramic Radiography Images
 
Creator Bineshwor Singh, Sanabam
Laishram, Anuradha
Thongam, Khelchandra
Manglem Singh, Kh
 
Subject Decision tree
Dental diagnosis
Feature extraction
Image pre-processing
Oral classification
 
Description 531-543
Detection and classification of tooth types and anomalies is crucial for accurate dental assessment, treatment planning,
and overall oral health preservation. The integration of machine learning expedites the classification of tooth types and
anomalies with lesser manual intervention, streamlining the diagnostic process and enhancing overall efficiency in dental
healthcare. This study addresses the automated classification of oral types and anomalies in panoramic radiograph images
which is a challenging task due to the complexity and variability of oral conditions. We propose an innovative approach
using hyperparameter-optimized Random Forest ensemble learning achieved through Genetic Algorithm. Various preprocessing
techniques are applied to enhance data quality by removing null values and minimizing noise in image data
followed by feature extraction. The model consists of 10 Decision Trees trained on various subsets, with hyperparameters
systematically optimized using a Genetic Algorithm. Outcomes from individual Decision Trees are aggregated through
majority voting. Comprehensive experimental assessment, including an 80-5-15 training-validation-testing split, results in
an impressive 98% accuracy. This highlights the effectiveness and superiority of our approach and demonstrates the
potential of Random Forest ensemble learning for automated accurate classification, with the added benefit of Genetic
Algorithm-driven hyperparameter tuning for improved model performance. Our approach may be applied in real-time for
telemedicine and can help dental professionals make decisions about prevention, diagnosis, and treatment planning.
 
Date 2024-05-06T11:09:31Z
2024-05-06T11:09:31Z
2024-05
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63853
https://doi.org/10.56042/jsir.v83i5.3994
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(5) [May 2024]