A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance

Yıl 2023, Cilt: 27 Sayı: 4, 872 - 886, 25.08.2023

Serkan Düzayak , Muhammed Kürşad Uçar

https://doi.org/10.16984/saufenbilder.1216668

Cited By: 1

Öz

Onychomycosis is the most common nail fungus disease in clinical practice worldwide, caused by the localization of various fungal agents, including dermatophytes, on the nail. The tests traditionally used for diagnosing onychomycosis are native examination, histopathological examination with periodic acid Schiff (PAS) staining, and nail culture. There is no gold standard method for diagnosing the disease, and the diagnosis process is time-consuming, costly, and quite laborious. Today, new technologies are needed to detect onychomycosis via AI-based ML to reduce the clinician and laboratory-induced error rate and increase diagnostic sensitivity and reliability. The present study aimed to design a decision support system to help the specialist doctor detect toenail fungus with artificial intelligence-based image processing techniques. The toenail images were taken by any camera initially from the individuals referred to the clinic. The image is divided into 12 RGB channels. Three hundred features were removed from each channel as 25 in the time domain. The best features were selected through feature selection algorithms in the next step to increase the performance and reduce the number of features, and models were created by algorithm classification. The average performance values of all proposed models, accuracy, sensitivity, and specificity, are 89.65, 0.9, and 0.89, respectively. The performance values of the most successful model-created accuracy, sensitivity, and specificity are 97.25, 0.96, and 0.98, respectively. Although the proposed method, according to the findings obtained in the study, has many advantages compared to the literature, it can be used as a decision support system for clinician diagnosis.

Anahtar Kelimeler

Onychomycosis, nail fungus, image processing, artificial intelligence, machine learning

Kaynakça

• A. K. Gupta, G. Gupta, H. C. Jain, C. W. Lynde, K. A. Foley, D. Daigle, E. A. Cooper, R. C. Summerbell., “The prevalence of unsuspected onychomycosis and its causative organisms in a multicentre Canadian sample of 30 000 patients visiting physicians’ offices,” Journal of the European Academy of Dermatology and Venereology, vol. 30, no. 9, pp. 1567–1572, Sep. 2016.
• A. K. Gupta, R. R. Mays, S. G. Versteeg, B. M. Piraccini, A. Takwale, A. Shemer, M. Babaev, C. Grover, N. G. Di Chiacchio, P. R. O. Taborda, V. B. A. Taborda, Neil H. Shear, V. Piguet, A. Tosti, “Global perspectives for the management of onychomycosis,” International Journal of Dermatology, vol. 58, no. 10, pp. 1118–1129, Oct. 2019.
• M. Papini, B. M. Piraccini, E. Difonzo, A. Brunoro, “Epidemiology of onychomycosis in Italy: prevalence data and risk factor identification,” Mycoses, vol. 58, no. 11, pp. 659–664, Nov. 2015.
• C. R. Stewart, , L. Algu, R. Kamran, C. F. Leveille, K. Abid, C. Rae, S. R. Lipner, “Effect of onychomycosis and treatment on patient-reported quality-of-life outcomes: A systematic review,” Journal of the American Academy of Dermatology, vol. 85, no. 5, pp. 1227–1239, Nov. 2021.
• V. Velasquez-Agudelo, J. A. Cardona-Arias, “Meta-analysis of the utility of culture, biopsy, and direct KOH examination for the diagnosis of onychomycosis,” BMC Infectious Diseases, vol. 17, no. 1, pp. 1–11, Feb. 2017.
• S. B. Lunge, N. S. Shetty, V. R. Sardesai, P. Karagaiah, P. S. Yamauchi, J. M. Weinberg, L. Kircik, M. Giulini, M. Goldust, “Therapeutic application of machine learning in psoriasis: A Prisma systematic review,” Journal of Cosmetic Dermatology, 2022.
• D. T. Hogarty, J. C. Su, K. Phan, M. Attia, M. Hossny, S. Nahavandi, P. Lenane, F. J. Moloney, A. Yazdabadi, “Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review,” American Journal of Clinical Dermatology 2019 21:1, vol. 21, no. 1, pp. 41–47, Jul. 2019.
• S. S. Han, G. H. Park, W. Lim, M. S. Kim, J. I. Na, I. Park, S. E. Chang, “Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network,” PLoS One, vol. 13, no. 1, p. e0191493, Jan. 2018.
• X. Zhu, B. Zheng, W. Cai, J. Zhang, S. Lu, X. Li, L. Xi, Y. Kong, “Deep learning-based diagnosis models for onychomycosis in dermoscopy,” Mycoses, vol. 65, no. 4, pp. 466–472, Apr. 2022.
• A. De, A. Sarda, S. Gupta, S. Das, “Use of artificial intelligence in dermatology,” Indian Journal of Dermatology, vol. 65, no. 5, p. 352, 2020.
• E. Gustafson, J. Pacheco, F. Wehbe, J. Silverberg, W. Thompson, “A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records,” Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, pp. 83–90, Sep. 2017.
• A. Martorell, A. Martin-Gorgojo, E. Ríos-Viñuela, J. M. Rueda-Carnero, F. Alfageme, R. Taberner, “Artificial Intelligence in Dermatology: A Threat or an Opportunity?,” Actas Dermosifiliogr, vol. 113, no. 1, pp. 30–46, Jan. 2022.
• S. S. Han, M. S. Kim, W. Lim, G. H. Park, I. Park, S. E. Chang, “Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm,” Journal of Investigative Dermatology, vol. 138, no. 7, pp. 1529–1538, Jul. 2018.
• A. Yilmaz, R. Varol, F. Goktay, G. Gencoglan, A. A. Demircali, B. Dilsizoglu, H. Uvet, “Deep Convolutional Neural Networks for Onychomycosis Detection,” Jun. 2021.
• J. Shaikh, R. Khan, Y. Ingle, N. Shaikh, “Improved skin cancer detection using CNN,” International journal of health sciences (Qassim), pp. 14347–14360, Jun. 2022.
• P. Puri, N. Comfere, L. A. Drage, H. Shamim, S. A. Bezalel, M. R. Pittelkow, M. D. P. Davis, M. Wang, A. R. Mangold, M. M. Tollefson, J. S. Lehman, A. Meves, J. A. Yiannias, C. C. Otley, R. E. Carter, O. Sokumbi, M. R. Hall, A. G. Bridges, D. H. Murphree, “Deep learning for dermatologists: Part II. Current applications,” Journal of the American Academy of Dermatology, vol. 0, no. 0, 2020.
• F. Lussier, V. Thibault, B. Charron, G. Q. Wallace, J. F. Masson, “Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering,” TrAC Trends in Analytical Chemistry, vol. 124, p. 115796, Mar. 2020.
• A. Nogales, Á. J. García-Tejedor, D. Monge, J. S. Vara, C. Antón, “A survey of deep learning models in medical therapeutic areas,” Artificial Intelligence in Medicine, vol. 112, p. 102020, Feb. 2021.
• X. Liu, L. Faes, A. U. Kale, S. K. Wagner, D. J. Fu, A. Bruynseels, T. Mahendiran, G. Moraes, M. Shamdas, C. Kern, J. R. Ledsam, M. K. Schmid, K. Balaskas, E. J. Topol, L. M. Bachmann, P. A. Keane, A. K. Denniston, “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,” The Lancet Digital Health, vol. 1, no. 6, pp. e271–e297, Oct. 2019.
• R. Nijhawan, R. Verma, Ayushi, S. Bhushan, R. Dua, A. Mittal, “An integrated deep learning framework approach for nail disease identification,” in Proceedings - 13th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2017, Apr. 2018, vol. 2018-January, pp. 197–202.
• J. Shaikh, R. Khan, Y. Ingle, N. Shaikh, “Improved skin cancer detection using CNN,” International journal of health sciences (Qassim), pp. 14347–14360, Jun. 2022.
• R. Regin, Reddy G, K. C. S. G., J. CVN, “Nail Disease Detection and Classification Using Deep Learning,” Central Asian Journal of Medical And Natural Science, vol. 3, no. 3, pp. 574–594, 2022. R. H. Chen, M. Snorrason, S. M. Enger, E. Mostafa, J. M. Ko, V. Aoki, J. Bowling, “Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology,” Telemedicine and e-Health, vol. 22, no. 1, pp. 45–50, Jan. 2016.
• T. S. Indi, Y. A. Gunge, “Early Stage Disease Diagnosis System Using Human Nail Image Processing,” International Journal of Information Technology and Computer Science, vol. 8, no. 7, pp. 30–35, Jul. 2016.
• A. Kanchna, D. Navanisha, V. Pavithra, D. Reshika, U. G. Scholar, “Early Stage Diseases Diagnosis using Human Nail in Image Processing,” International Journal of Information Technology and Computer Science, 2021.
• Y. J. Kim, S. S. Han, H. J. Yang, S. E. Chang, “Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis,” PLoS One, vol. 15, no. 6, p. e0234334, Jun. 2020.
• M. K. Uçar, “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm,” Journal of Intelligent Systems: Theory and Applications, vol. 2, no. 1, pp. 7–12, Jul. 2019.
• E. Melekoglu, U. Kocabicak, M. K. Uçar, C. Bilgin, M. R. Bozkurt, M. Cunkas, “A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence,” PeerJ Computer Science, vol. 8, p. e1188, Dec. 2022.
• M. Nour, D. Kandaz, M. K. Ucar, K. Polat, A. Alhudhaif, “Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
• M. Akman, M. K. Uçar, Z. Uçar, K. Uçar, B. Baraklı, M. R. Bozkurt, “Determination of Body Fat Percentage by Gender Based with Photoplethysmography Signal Using Machine Learning Algorithm,” IRBM, vol. 43, no. 3, pp. 169–186, Jun. 2022.
• M. K. Uçar, K. Uçar, Z. Uçar, M. R. Bozkurt, “Determination gender-based hybrid artificial intelligence of body muscle percentage by photoplethysmography signal,” Computational and Mathematical Methods in Medicine, vol. 224, p. 107010, Sep. 2022.
• R. Alpar, Spor, Applied Statistic and Validation – Reliability, Detay Publisher, 2016
• K. He, X. Zhang, S. Ren, J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” in 2015 IEEE International Conference on Computer Vision (ICCV), Dec. 2015, pp. 1026–1034.
• X. Glorot, Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks.” JMLR Workshop and Conference Proceedings, pp. 249–256, Mar. 31, 2010.
• C. Cortes, V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, pp. 273–297, 1995.
• M. Vogt, V. Kecman, “Active-Set Methods for Support Vector Machines,” in Support Vector Machines: Theory and Applications, vol. 177, Berlin, Heidelberg: Springer, 2005, pp. 133–158.
• P. Bühlmann, “Bagging, Boosting and Ensemble Learning,” in Handbook of Computational Statistics: Concepts and Methods, J. E. Gentle, W. K. Härdle, and Y. Mori, Eds. Springer-Verlag Berlin Heidelberg, 2012, pp. 1–38.
• T. G. Dietterich, “Ensemble Methods in Machine Learning,” International Workshop on Multiple Classifier Systems MCS 2000: Multiple Classifier Systems. Springer, pp. 1–15, 2000.
• L. Rokach, A. Schclar, E. Itach, “Ensemble methods for multi-label classification,” Expert Systems with Applications, vol. 41, no. 16, pp. 7507–7523, Nov. 2014.
• I. Topal, M. K. Ucar, “Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists,” IEEE Access, vol. 7, 2019.
• X. Zhang, W. Dahu, “Application of artificial intelligence algorithms in image processing,” Journal of Visual Communication and Image Representation, vol. 61, pp. 42–49, May 2019.
• P. Mamoshina, A. Vieira, E. Putin, A. Zhavoronkov, “Applications of Deep Learning in Biomedicine,” Molecular Pharmaceutics, vol. 13, no. 5, pp. 1445–1454, May 2016.
• C. Shen, D. Nguyen, Z. Zhou, S. B. Jiang, B. Dong, X. Jia, “An introduction to deep learning in medical physics: advantages, potential, and challenges,” Physics in Medicine & Biology, vol. 65, no. 5, p. 05TR01, Mar. 2020.
• Y. Chen, H. Liu, Z. Liu, Y. Xie, Y. Yao, X. Xing, H. Ma, “Development and validation of the interpretability analysis system based on deep learning model for smart image follow-up of nail pigmentation,” Annals of Translational Medicine, vol. 10, no. 10, pp. 551–551, May 2022.
• S. S. Lim, J. Ohn, J. H. Mun, “Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence,” Frontiers in Medicine (Lausanne), vol. 8, p. 460, Apr. 2021.