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Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection

Year 2025, , 58 - 75, 28.03.2025

Furkancan Demircan , Murat Ekinci , Zafer Cömert , Eyup Gedikli

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

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

Machine learning Optimization Feature extraction Feature selection

References

  • W. H. Organization, Primary ear and hearing care training manual. Genève, Switzerland: World Health Organization, 2023.
  • Institute of Electrical and Electronics Engineers and Manav Rachna International Institute of Research and Studies, Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: trends, perspectives and prospects : COMITCON-2019 : 14th-16th February 2019.
  • Z. Wu et al., “Deep Learning for Classification of Pediatric Otitis Media,” Laryngoscope, vol. 131, no. 7, pp. E2344–E2351, Jul. 2021, doi: 10.1002/lary.29302.
  • J. V. Sundgaard et al., “Deep metric learning for otitis media classification,” Med Image Anal, vol. 71, Jul. 2021, doi: 10.1016/j.media.2021.102034.
  • A. Alhudhaif, Z. Cömert, and K. Polat, “Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm,” PeerJ Comput Sci, vol. 7, pp. 1–22, 2021, doi: 10.7717/PEERJ-CS.405.
  • T. T. Tran, T. Y. Fang, V. T. Pham, C. Lin, P. C. Wang, and M. T. Lo, “Development of an automatic diagnostic algorithm for pediatric otitis media,” Otology and Neurotology, vol. 39, no. 8, pp. 1060–1065, 2018, doi: 10.1097/MAO.0000000000001897.
  • H. C. Myburgh, S. Jose, D. W. Swanepoel, and C. Laurent, “Towards low cost automated smartphone- and cloud-based otitis media diagnosis,” Biomed Signal Process Control, vol. 39, pp. 34–52, Jan. 2018, doi: 10.1016/j.bspc.2017.07.015.
  • K. K. Mohammed, A. E. Hassanien, and H. M. Afify, “Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture,” J Digit Imaging, vol. 35, no. 4, pp. 947–961, Aug. 2022, doi: 10.1007/s10278-022-00617-8.
  • Y. M. Wang et al., “Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography,” Ear Hear, pp. 669–677, 2020, doi: 10.1097/AUD.0000000000000794.
  • D. Cha, C. Pae, S. B. Seong, J. Y. Choi, and H. J. Park, “Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database,” EBioMedicine, vol. 45, pp. 606–614, Jul. 2019, doi: 10.1016/j.ebiom.2019.06.050.
  • Y.-C. Chen et al., “Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study,” EClinicalMedicine, vol. 51, no. 201, p. 101543, 2022, doi: 10.1016/j.
  • J. V. Sundgaard et al., “A Deep Learning Approach for Detecting Otitis Media From Wideband Tympanometry Measurements,” IEEE J Biomed Health Inform, vol. 26, no. 7, pp. 2974–2982, Jul. 2022, doi: 10.1109/JBHI.2022.3159263.
  • K. Tsutsumi et al., “A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images,” Otol Neurotol, vol. 42, no. 9, pp. e1382–e1388, Oct. 2021, doi: 10.1097/MAO.0000000000003210.
  • Z. Wang et al., “Structure-aware deep learning for chronic middle ear disease,” Expert Syst Appl, vol. 194, May 2022, doi: 10.1016/j.eswa.2022.116519.
  • J. Zeng et al., “A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images,” European Archives of Oto-Rhino-Laryngology, vol. 280, no. 4, pp. 1621–1627, Apr. 2023, doi: 10.1007/s00405-022-07632-z.
  • A. R. Habib et al., “Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-31921-0.
  • V. T. Pham, T. T. Tran, P. C. Wang, P. Y. Chen, and M. T. Lo, “EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images,” Artif Intell Med, vol. 115, May 2021, doi: 10.1016/j.artmed.2021.102065.
  • Z. Cömert, A. Sbrollini, F. Demircan, and L. Burattini, “Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis,” Neural Comput Appl, vol. 36, no. 36, pp. 23113–23129, Dec. 2024, doi: 10.1007/s00521-024-10457-y.
  • F. Demircan, M. Ekinci, and Z. Cömert, “Enhancing intra-aural disease classification with attention-based deep learning models,” Neural Comput Appl, Jan. 2025, doi: 10.1007/s00521-025-10990-4.
  • N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, pp. 886–893 vol. 1. doi: 10.1109/CVPR.2005.177.
  • F. Marini and B. Walczak, “Particle swarm optimization (PSO). A tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015, doi: https://doi.org/10.1016/j.chemolab.2015.08.020.
  • S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016, doi: https://doi.org/10.1016/j.advengsoft.2016.01.008.
  • A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine Predators Algorithm: A nature-inspired metaheuristic,” Expert Syst Appl, vol. 152, p. 113377, 2020, doi: https://doi.org/10.1016/j.eswa.2020.113377.
  • T. T. Nguyen, H. J. Wang, T. K. Dao, J. S. Pan, J. H. Liu, and S. Weng, “An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations,” IEEE Access, vol. 8, pp. 226754–226772, 2020, doi: 10.1109/ACCESS.2020.3045975.
  • B. ERGEN and M. E. SERTKAYA, “Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti,” European Journal of Science and Technology, Mar. 2022, doi: 10.31590/ejosat.1082297.
  • H. Byun et al., “An assistive role of a machine learning network in diagnosing middle ear diseases,” J Clin Med, vol. 10, no. 15, Aug. 2021, doi: 10.3390/jcm10153198.
  • M. Uçar, K. Akyol, Atila, and E. Uçar, “Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM,” IRBM, vol. 43, no. 3, pp. 187–197, Jun. 2022, doi: 10.1016/j.irbm.2021.01.001.
  • D. Livingstone and J. Chau, “Otoscopic diagnosis using computer vision: An automated machine learning approach,” Laryngoscope, vol. 130, no. 6, pp. 1408–1413, Jun. 2020, doi: 10.1002/lary.28292.

Year 2025, , 58 - 75, 28.03.2025

Furkancan Demircan , Murat Ekinci , Zafer Cömert , Eyup Gedikli

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

Abstract

References

  • W. H. Organization, Primary ear and hearing care training manual. Genève, Switzerland: World Health Organization, 2023.
  • Institute of Electrical and Electronics Engineers and Manav Rachna International Institute of Research and Studies, Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: trends, perspectives and prospects : COMITCON-2019 : 14th-16th February 2019.
  • Z. Wu et al., “Deep Learning for Classification of Pediatric Otitis Media,” Laryngoscope, vol. 131, no. 7, pp. E2344–E2351, Jul. 2021, doi: 10.1002/lary.29302.
  • J. V. Sundgaard et al., “Deep metric learning for otitis media classification,” Med Image Anal, vol. 71, Jul. 2021, doi: 10.1016/j.media.2021.102034.
  • A. Alhudhaif, Z. Cömert, and K. Polat, “Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm,” PeerJ Comput Sci, vol. 7, pp. 1–22, 2021, doi: 10.7717/PEERJ-CS.405.
  • T. T. Tran, T. Y. Fang, V. T. Pham, C. Lin, P. C. Wang, and M. T. Lo, “Development of an automatic diagnostic algorithm for pediatric otitis media,” Otology and Neurotology, vol. 39, no. 8, pp. 1060–1065, 2018, doi: 10.1097/MAO.0000000000001897.
  • H. C. Myburgh, S. Jose, D. W. Swanepoel, and C. Laurent, “Towards low cost automated smartphone- and cloud-based otitis media diagnosis,” Biomed Signal Process Control, vol. 39, pp. 34–52, Jan. 2018, doi: 10.1016/j.bspc.2017.07.015.
  • K. K. Mohammed, A. E. Hassanien, and H. M. Afify, “Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture,” J Digit Imaging, vol. 35, no. 4, pp. 947–961, Aug. 2022, doi: 10.1007/s10278-022-00617-8.
  • Y. M. Wang et al., “Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography,” Ear Hear, pp. 669–677, 2020, doi: 10.1097/AUD.0000000000000794.
  • D. Cha, C. Pae, S. B. Seong, J. Y. Choi, and H. J. Park, “Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database,” EBioMedicine, vol. 45, pp. 606–614, Jul. 2019, doi: 10.1016/j.ebiom.2019.06.050.
  • Y.-C. Chen et al., “Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study,” EClinicalMedicine, vol. 51, no. 201, p. 101543, 2022, doi: 10.1016/j.
  • J. V. Sundgaard et al., “A Deep Learning Approach for Detecting Otitis Media From Wideband Tympanometry Measurements,” IEEE J Biomed Health Inform, vol. 26, no. 7, pp. 2974–2982, Jul. 2022, doi: 10.1109/JBHI.2022.3159263.
  • K. Tsutsumi et al., “A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images,” Otol Neurotol, vol. 42, no. 9, pp. e1382–e1388, Oct. 2021, doi: 10.1097/MAO.0000000000003210.
  • Z. Wang et al., “Structure-aware deep learning for chronic middle ear disease,” Expert Syst Appl, vol. 194, May 2022, doi: 10.1016/j.eswa.2022.116519.
  • J. Zeng et al., “A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images,” European Archives of Oto-Rhino-Laryngology, vol. 280, no. 4, pp. 1621–1627, Apr. 2023, doi: 10.1007/s00405-022-07632-z.
  • A. R. Habib et al., “Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-31921-0.
  • V. T. Pham, T. T. Tran, P. C. Wang, P. Y. Chen, and M. T. Lo, “EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images,” Artif Intell Med, vol. 115, May 2021, doi: 10.1016/j.artmed.2021.102065.
  • Z. Cömert, A. Sbrollini, F. Demircan, and L. Burattini, “Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis,” Neural Comput Appl, vol. 36, no. 36, pp. 23113–23129, Dec. 2024, doi: 10.1007/s00521-024-10457-y.
  • F. Demircan, M. Ekinci, and Z. Cömert, “Enhancing intra-aural disease classification with attention-based deep learning models,” Neural Comput Appl, Jan. 2025, doi: 10.1007/s00521-025-10990-4.
  • N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, pp. 886–893 vol. 1. doi: 10.1109/CVPR.2005.177.
  • F. Marini and B. Walczak, “Particle swarm optimization (PSO). A tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015, doi: https://doi.org/10.1016/j.chemolab.2015.08.020.
  • S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016, doi: https://doi.org/10.1016/j.advengsoft.2016.01.008.
  • A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine Predators Algorithm: A nature-inspired metaheuristic,” Expert Syst Appl, vol. 152, p. 113377, 2020, doi: https://doi.org/10.1016/j.eswa.2020.113377.
  • T. T. Nguyen, H. J. Wang, T. K. Dao, J. S. Pan, J. H. Liu, and S. Weng, “An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations,” IEEE Access, vol. 8, pp. 226754–226772, 2020, doi: 10.1109/ACCESS.2020.3045975.
  • B. ERGEN and M. E. SERTKAYA, “Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti,” European Journal of Science and Technology, Mar. 2022, doi: 10.31590/ejosat.1082297.
  • H. Byun et al., “An assistive role of a machine learning network in diagnosing middle ear diseases,” J Clin Med, vol. 10, no. 15, Aug. 2021, doi: 10.3390/jcm10153198.
  • M. Uçar, K. Akyol, Atila, and E. Uçar, “Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM,” IRBM, vol. 43, no. 3, pp. 187–197, Jun. 2022, doi: 10.1016/j.irbm.2021.01.001.
  • D. Livingstone and J. Chau, “Otoscopic diagnosis using computer vision: An automated machine learning approach,” Laryngoscope, vol. 130, no. 6, pp. 1408–1413, Jun. 2020, doi: 10.1002/lary.28292.
There are 28 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Furkancan Demircan 0000-0001-8096-5731

Murat Ekinci 0000-0001-9326-8425

Zafer Cömert 0000-0001-5256-7648

Eyup Gedikli 0000-0002-7212-5457

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 2025

Cite

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 Demircan F, Ekinci M, Cömert Z, Gedikli E. Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. SAUCIS. March 2025;8(1):58-75. doi:10.35377/saucis.1579003
Chicago Demircan, Furkancan, Murat Ekinci, Zafer Cömert, and Eyup Gedikli. “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”. Sakarya University Journal of Computer and Information Sciences 8, no. 1 (March 2025): 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 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, 2025, doi: 10.35377/saucis...1579003.
ISNAD Demircan, Furkancan et al. “Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 2025), 58-75. https://doi.org/10.35377/saucis. 1579003.
JAMA 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, 2025, pp. 58-75, doi:10.35377/saucis. 1579003.
Vancouver Demircan F, Ekinci M, Cömert Z, Gedikli E. Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection. SAUCIS. 2025;8(1):58-75.


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