Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection

Furkancan Demircan; Murat Ekinci; Zafer Cömert; Eyup Gedikli

doi:10.35377/saucis...1579003

EN

Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection

Abstract

Ear diseases are characterized by various symptoms, including balance disturbances, delayed speech development in children, headaches, fever, and hearing loss. To prevent further complications, these conditions must be diagnosed and treated promptly. The traditional diagnostic method has been an otoscope examination by otolaryngologists. However, the accuracy of this approach is contingent upon the clinician's expertise and the quality of the equipment used, which can render it susceptible to misdiagnosis. Incorrect diagnoses may result in the administration of antibiotics unnecessarily, disease progression, and other adverse consequences. This study aims to evaluate the efficacy of computationally efficient machine learning models in classifying ear disease images. To enhance classification accuracy, a Histogram of Oriented Gradients (HOG) was employed for feature extraction and optimization algorithms were utilized for feature selection. The Whale Optimization Algorithm (WOA) effectively selected informative features for the k-Nearest Neighbors (kNN) model, achieving a classification accuracy of 92.6%. Furthermore, the Support Vector Machine (SVM) model achieved an accuracy of 92% using a feature map comprising features selected by a range of optimization algorithms. The experimental findings emphasize the potential of strategic feature selection in enhancing the performance of classical machine learning models for ear disease classification. By employing computationally efficient techniques such as HOG and optimization algorithms, these models can attain classification accuracies that are on par with those of more resource-intensive deep learning approaches. Such developments facilitate the creation of accessible and efficient diagnostic tools, particularly beneficial in resource-constrained clinical settings. The findings of this study provide a basis for further research to enhance the diagnostic precision of machine learning-based techniques in medical imaging.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Furkancan Demircan ^*
0000-0001-8096-5731
Türkiye

Murat Ekinci
0000-0001-9326-8425
Türkiye

Zafer Cömert
0000-0001-5256-7648
Türkiye

Eyup Gedikli
0000-0002-7212-5457
Türkiye

Early Pub Date

March 27, 2025

Publication Date

March 28, 2025

Submission Date

November 4, 2024

Acceptance Date

February 24, 2025

Published in Issue

Year 1970 Volume: 8 Number: 1

DOI

https://doi.org/10.35377/saucis...1579003

IZ

https://izlik.org/JA25NB39AP

Cite

RIS / Bibtex

APA

Demircan, F., Ekinci, M., Cömert, Z., & Gedikli, E. (2025). Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. Sakarya University Journal of Computer and Information Sciences, 8(1), 58-75. https://doi.org/10.35377/saucis...1579003

AMA

1.Demircan F, Ekinci M, Cömert Z, Gedikli E. Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. SAUCIS. 2025;8(1):58-75. doi:10.35377/saucis.1579003

Chicago

Demircan, Furkancan, Murat Ekinci, Zafer Cömert, and Eyup Gedikli. 2025. “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”. Sakarya University Journal of Computer and Information Sciences 8 (1): 58-75. https://doi.org/10.35377/saucis. 1579003.

EndNote

Demircan F, Ekinci M, Cömert Z, Gedikli E (March 1, 2025) Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. Sakarya University Journal of Computer and Information Sciences 8 1 58–75.

IEEE

[1]F. Demircan, M. Ekinci, Z. Cömert, and E. Gedikli, “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”, SAUCIS, vol. 8, no. 1, pp. 58–75, Mar. 2025, doi: 10.35377/saucis...1579003.

ISNAD

Demircan, Furkancan - Ekinci, Murat - Cömert, Zafer - Gedikli, Eyup. “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 1, 2025): 58-75. https://doi.org/10.35377/saucis. 1579003.

JAMA

1.Demircan F, Ekinci M, Cömert Z, Gedikli E. Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. SAUCIS. 2025;8:58–75.

MLA

Demircan, Furkancan, et al. “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, Mar. 2025, pp. 58-75, doi:10.35377/saucis. 1579003.

Vancouver

1.Furkancan Demircan, Murat Ekinci, Zafer Cömert, Eyup Gedikli. Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. SAUCIS. 2025 Mar. 1;8(1):58-75. doi:10.35377/saucis. 1579003