Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

Soltani, Roozbeh; Jaouadi, Amine

doi:10.36227/techrxiv.20408919

2022

DOI: 10.36227/techrxiv.20408919

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Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

Roozbeh Soltani¹,

Amine Jaouadi²

Abstract: <p>The dataset has been done at Gilan University of Medical Sciences. It included 210 clinical images of oral mucosa, 154 images of which were related to the three studied lesions: 50 cases of erosive lichen planus and ulcerative, 53 cases of leukoplakia, 51 cases of oral SCC, and 56 images were related to healthy mucosa. The dataset contained 30 features.</p>

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New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

Vijh,

Gupta,

Kumar

et al. 2023

Expert Systems

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Dentistry diseases are worldwide concern with the fact that 5% of medical budget is spent on it. Dental diagnosis requires frequent visits to clinic and multiple personal check‐ups by an expert. This delays the process of diagnosis as well as introduces the danger of oral infection spread. Automation is boon to speed up the process of diagnosis, reduce expert's involvement and help in handling volume of patients. In situations like pandemic as seen in this decade, computer based automatic systems in health have proven their importance and necessity. For dental diagnosis, images are useful tool for better anatomical views and accurate decision of treatment. However, manual dental analysis increases load on dentists for initial check‐up that can be easily performed with automated systems or self‐kits. Oral cavity is a dental illness, if not identified and treated on time may lead to other serious ailments. This work presents a framework that performs tooth image classification for cavity and non‐cavity with new bag of features (NBoF) method. NBoF method is an attempt to improve the performance of bag of features (BoF) using the proposed reinforcement Aquila optimization (RAO) and weighted Bayesian Gaussian mixture modelling (WBGMM). An analysis of the performance of the NBoF using the proposed RAO and WBGMM is conducted using standard metrics. The comparative study of results proves that the proposed NBoF method outperforms the existing state‐of‐the‐art algorithms.

show abstract

New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

Vijh,

Gupta,

Kumar

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

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Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

Cited by 1 publication

References 22 publications

New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

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