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ÇOĞUNLUK OYUNA DAYALI TOPLULUK MODELİ İLE TIRNAK HASTALIKLARININ TESPİTİ

Year 2023, Volume: 26 Issue: 1, 250 - 260, 15.03.2023

Senar Ali Yamaç , Orhun Kuyucuoğlu , Şeyma Begüm Köseoğlu , Sezer Ulukaya

https://doi.org/10.17780/ksujes.1224006

Abstract

Tırnak hastalıkları, insanın yaşam kalitesini ciddi şekilde etkileyebilen bozukluklardır. Gelişen hesaplamalı yöntemler ve teknoloji ile tırnaktaki anomaliler hızlı ve girişimsiz bir şekilde tespit edilebilmektedir. Bu çalışma, farklı derin öğrenme ağlarının sonuçlarını topluluk öğrenme yöntemiyle birleştirerek daha iyi performans sağlayan bir model önermektedir. 7 farklı derin öğrenme mimarisinin performansı, 17 hastalık sınıfı içeren bir veritabanı kullanılarak incelenmiştir. Önerilen yöntem % 75 doğruluk elde etti ve bireysel derin öğrenme mimarilerine kıyasla kesinlik ve duyarlılık metriklerinde önemli artışlar sağladı. Önerilen model geliştirilebilecek bir mobil uygulama sayesinde, büyük ölçekli taramalarda tıp profesyonelleri için yardımcı bir karar destek sistemi olarak kullanılabilecektir. Sonuçlara bakıldığında en çok kullandığımız uzuvlarımızdan biri olan eldeki tırnaklarımızda meydana gelen hastalıkların (uzaktan) erken tespit edilmesinin hastane ziyaretlerini ve maliyetleri azaltabileceğini öngörüyoruz. Ayrıca önerilen yöntem cilt hastalıkları ve ben analizi için kullanılan dermatoskopi cihazlarına entegre edilebilir.

Keywords

derin öğrenme tele sağlık tırnak hastalıkları çoğunluk oylaması topluluk modeli

References

  • Abdulhadi, J., Al-Dujaili, A., Humaidi, A. J., & Fadhel, M. A. R. (2021). Human nail diseases classification based on transfer learning. ICIC Express Letters, 15(12), 1271–1282.
  • Akcan, F., & Sertbaş, A. (2021). Topluluk Öğrenmesi Yöntemleri ile Göğüs Kanseri Teşhisi. Electronic Turkish Studies, 16(2). https://doi.org/10.7827/TurkishStudies
  • Azad, M. M., Ganapathy, A., Vadlamudi, S., & Paruchuri, H. (2021). Medical diagnosis using deep learning techniques: a research survey. Annals of the Romanian Society for Cell Biology, 25(6), 5591-5600.
  • Barsha, N. A., Rahman, A., & Mahdy, M. R. C. (2021). Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models. Computers in Biology and Medicine, 139, 104931.
  • Begum, M., Dhivya, A., Krishnan, A. J., & Keerthana, S. D. (2021, June). Automated Detection of skin and nail disorders using Convolutional Neural Networks. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1309-1316). IEEE.
  • Chelidze, K., & Lipner, S. R. (2018). Nail changes in alopecia areata: an update and review. International Journal of Dermatology, 57(7), 776-783.
  • Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2021). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 1-18.
  • Fawcett, R. S., Linford, S., & Stulberg, D. L. (2004). Nail abnormalities: clues to systemic disease. American Family Physician, 69(6), 1417-1424.
  • Gülcü, A., & Kuş, Z. (2019). A Survey of Hyper-parameter Optimization Methods in Convolutional Neural Networks. Gazi Üniversitesi Fen Bilimleri Dergisi, 7(2), 503-522.
  • Ilhan, H. O., Serbes, G., & Aydin, N. (2022). Decision and feature level fusion of deep features extracted from public COVID-19 data-sets. Applied Intelligence, 52(8), 8551-8571.
  • Indi, T. S., & Gunge, Y. A. (2016). Early stage disease diagnosis system using human nail image processing. IJ Information Technology and Computer Science, 7, 30-35. https://doi.org/10.5815/ijitcs.2016.07.05
  • Jiang, H., Xu, J., Shi, R., Yang, K., Zhang, D., Gao, M., ... & Qian, W. (2020, July). A multi-label deep learning model with interpretable grad-CAM for diabetic retinopathy classification. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1560-1563). IEEE.
  • Mehra, M., D'Costa, S., D'Mello, R., George, J., & Kalbande, D. R. (2021, January). Leveraging Deep Learning for Nail Disease Diagnostic. In 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE) (pp. 1-5). IEEE.
  • Nijhawan, R., Verma, R., Bhushan, S., Dua, R., & Mittal, A. (2017, December). An integrated deep learning framework approach for nail disease identification. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 197-202). IEEE.
  • Ocal, H., & Barisci, N. (2022). Prostate segmentation via dynamic fusion model. Arabian Journal for Science and Engineering, 47(8), 10211-10224. https://doi.org/10.1007/s13369-021-06502-w
  • Pandit, H., & Shah, D. M. (2013, March). A system for nail color analysis in healthcare. In 2013 International Conference on Intelligent Systems and Signal Processing (ISSP) (pp. 221-223). IEEE.
  • Rahman, M. T., & Dola, A. (2021, December). Automated Grading of Diabetic Retinopathy using DenseNet-169 Architecture. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT) (pp. 1-4). IEEE. https://doi.org/10.1109/EICT54103.2021.9733431
  • Reubenindustrustech (2022). Nail dataset. https://www.kaggle.com/reubenindustrustech Sadaei, H. J., e Silva, P. C. D. L., Guimarães, F. G., & Lee, M. H. (2019). Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175, 365-377.
  • Safira, L., Irawan, B., & Setianingsih, C. (2019, July). K-Nearest Neighbour Classification and Feature Extraction GLCM for Identification of Terry's Nail. In 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) (pp. 98-104). IEEE.
  • Sah, A. K., Bhusal, S., Amatya, S., Mainali, M., & Shakya, S. (2019, October). Dermatological diseases classification using image processing and deep neural network. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 381-386). IEEE.
  • Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446-2455. https://doi.org/10.1109/TII.2018.2864759.
  • Solmaz, R., Alkan, A., & Günay, M. (2020). Mobile diagnosis of thyroid based on ensemble classifier. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(3), 915-924. https://doi.org/10.24012/dumf.687898
  • Stivaktakis, R., Tsagkatakis, G., & Tsakalides, P. (2019). Deep learning for multilabel land cover scene categorization using data augmentation. IEEE Geoscience and Remote Sensing Letters, 16(7), 1031-1035.
  • Summers, C., & Dinneen, M. J. (2019, January). Improved mixed-example data augmentation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1262-1270). IEEE. https://doi.org/10.1109/WACV.2019.00139
  • Sünnetci, K. M., & Alkan, A. (2022a). Lung cancer detection by using probabilistic majority voting and optimization techniques. International Journal of Imaging Systems and Technology, 32(6), 2049-2065.
  • Sünnetci, K. M., & Alkan, A. (2022b). Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-Ray images. Expert Systems with Applications, 119430.
  • Taha, A. A., & Malebary, S. J. (2022). A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction. CMC-COMPUTERS MATERIALS & CONTINUA, 71(3), 6089-6105.
  • Tandel, G. S., Tiwari, A., & Kakde, O. G. (2021). Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Computers in Biology and Medicine, 135, 104564. https://doi.org/10.1016/j.compbiomed.2021.104564
  • Thahira Banu, V., & Devi, M. R. (2021). Hybrid classifier to classify the finger nail abnormalities. Informatıon Technology In Industry, 9(1), 549-555. https://doi.org/10.17762/itii.v9i1.168
  • Theckedath, D., & Sedamkar, R. R. (2020). Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science, 1(2), 1-7.
  • Xiao, M., Zhang, L., Shi, W., Liu, J., He, W., & Jiang, Z. (2021, September). A visualization method based on the Grad-CAM for medical image segmentation model. In 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) (pp. 242-247). IEEE.
  • Yamaç, S. A., Kuyucuoğlu, O., Köseoğlu, Ş. B., & Ulukaya, S. (2022, July). Deep learning based classification of human nail diseases using color nail images. In 2022 45th International Conference on Telecommunications and Signal Processing (TSP) (pp. 196-199). IEEE.
  • Yani, M. (2019, May). Application of transfer learning using convolutional neural network method for early detection of terry’s nail. In Journal of Physics: Conference Series (Vol. 1201, No. 1, p. 012052). IOP Publishing.

DETECTION OF NAIL DISEASES USING ENSEMBLE MODEL BASED ON MAJORITY VOTING

Year 2023, Volume: 26 Issue: 1, 250 - 260, 15.03.2023

Senar Ali Yamaç , Orhun Kuyucuoğlu , Şeyma Begüm Köseoğlu , Sezer Ulukaya

https://doi.org/10.17780/ksujes.1224006

Abstract

Nail diseases are disorders that can have serious effects on human quality of life. With the developing computational methods and technology, anomalies on the nail may be detected quickly and in a non-invasive way. This study proposes a model that provides better performance by combining the results of different deep learning networks with the ensemble learning method. The performance of 7 different deep learning architectures was examined using a database containing 17 disease classes. The proposed method achieved 75 % accuracy, resulting in significant increases in precision and recall metrics compared to individual deep-learning architectures. Thanks to a mobile application that will be developed, the proposed model for large-scale screening may be used as an assistive decision support system for medical professionals. When the results are observed, we predict that early detection of nail diseases (in a remote way) on the hand, which is one of our most used limbs, can reduce hospital visits and costs. In addition, the proposed method can be integrated into dermatoscopy devices used for skin diseases and mole analysis.

Keywords

Deep learning telehealth nail diseases majority voting ensemble learning

References

  • Abdulhadi, J., Al-Dujaili, A., Humaidi, A. J., & Fadhel, M. A. R. (2021). Human nail diseases classification based on transfer learning. ICIC Express Letters, 15(12), 1271–1282.
  • Akcan, F., & Sertbaş, A. (2021). Topluluk Öğrenmesi Yöntemleri ile Göğüs Kanseri Teşhisi. Electronic Turkish Studies, 16(2). https://doi.org/10.7827/TurkishStudies
  • Azad, M. M., Ganapathy, A., Vadlamudi, S., & Paruchuri, H. (2021). Medical diagnosis using deep learning techniques: a research survey. Annals of the Romanian Society for Cell Biology, 25(6), 5591-5600.
  • Barsha, N. A., Rahman, A., & Mahdy, M. R. C. (2021). Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models. Computers in Biology and Medicine, 139, 104931.
  • Begum, M., Dhivya, A., Krishnan, A. J., & Keerthana, S. D. (2021, June). Automated Detection of skin and nail disorders using Convolutional Neural Networks. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1309-1316). IEEE.
  • Chelidze, K., & Lipner, S. R. (2018). Nail changes in alopecia areata: an update and review. International Journal of Dermatology, 57(7), 776-783.
  • Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2021). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 1-18.
  • Fawcett, R. S., Linford, S., & Stulberg, D. L. (2004). Nail abnormalities: clues to systemic disease. American Family Physician, 69(6), 1417-1424.
  • Gülcü, A., & Kuş, Z. (2019). A Survey of Hyper-parameter Optimization Methods in Convolutional Neural Networks. Gazi Üniversitesi Fen Bilimleri Dergisi, 7(2), 503-522.
  • Ilhan, H. O., Serbes, G., & Aydin, N. (2022). Decision and feature level fusion of deep features extracted from public COVID-19 data-sets. Applied Intelligence, 52(8), 8551-8571.
  • Indi, T. S., & Gunge, Y. A. (2016). Early stage disease diagnosis system using human nail image processing. IJ Information Technology and Computer Science, 7, 30-35. https://doi.org/10.5815/ijitcs.2016.07.05
  • Jiang, H., Xu, J., Shi, R., Yang, K., Zhang, D., Gao, M., ... & Qian, W. (2020, July). A multi-label deep learning model with interpretable grad-CAM for diabetic retinopathy classification. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1560-1563). IEEE.
  • Mehra, M., D'Costa, S., D'Mello, R., George, J., & Kalbande, D. R. (2021, January). Leveraging Deep Learning for Nail Disease Diagnostic. In 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE) (pp. 1-5). IEEE.
  • Nijhawan, R., Verma, R., Bhushan, S., Dua, R., & Mittal, A. (2017, December). An integrated deep learning framework approach for nail disease identification. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 197-202). IEEE.
  • Ocal, H., & Barisci, N. (2022). Prostate segmentation via dynamic fusion model. Arabian Journal for Science and Engineering, 47(8), 10211-10224. https://doi.org/10.1007/s13369-021-06502-w
  • Pandit, H., & Shah, D. M. (2013, March). A system for nail color analysis in healthcare. In 2013 International Conference on Intelligent Systems and Signal Processing (ISSP) (pp. 221-223). IEEE.
  • Rahman, M. T., & Dola, A. (2021, December). Automated Grading of Diabetic Retinopathy using DenseNet-169 Architecture. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT) (pp. 1-4). IEEE. https://doi.org/10.1109/EICT54103.2021.9733431
  • Reubenindustrustech (2022). Nail dataset. https://www.kaggle.com/reubenindustrustech Sadaei, H. J., e Silva, P. C. D. L., Guimarães, F. G., & Lee, M. H. (2019). Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175, 365-377.
  • Safira, L., Irawan, B., & Setianingsih, C. (2019, July). K-Nearest Neighbour Classification and Feature Extraction GLCM for Identification of Terry's Nail. In 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) (pp. 98-104). IEEE.
  • Sah, A. K., Bhusal, S., Amatya, S., Mainali, M., & Shakya, S. (2019, October). Dermatological diseases classification using image processing and deep neural network. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 381-386). IEEE.
  • Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446-2455. https://doi.org/10.1109/TII.2018.2864759.
  • Solmaz, R., Alkan, A., & Günay, M. (2020). Mobile diagnosis of thyroid based on ensemble classifier. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(3), 915-924. https://doi.org/10.24012/dumf.687898
  • Stivaktakis, R., Tsagkatakis, G., & Tsakalides, P. (2019). Deep learning for multilabel land cover scene categorization using data augmentation. IEEE Geoscience and Remote Sensing Letters, 16(7), 1031-1035.
  • Summers, C., & Dinneen, M. J. (2019, January). Improved mixed-example data augmentation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1262-1270). IEEE. https://doi.org/10.1109/WACV.2019.00139
  • Sünnetci, K. M., & Alkan, A. (2022a). Lung cancer detection by using probabilistic majority voting and optimization techniques. International Journal of Imaging Systems and Technology, 32(6), 2049-2065.
  • Sünnetci, K. M., & Alkan, A. (2022b). Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-Ray images. Expert Systems with Applications, 119430.
  • Taha, A. A., & Malebary, S. J. (2022). A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction. CMC-COMPUTERS MATERIALS & CONTINUA, 71(3), 6089-6105.
  • Tandel, G. S., Tiwari, A., & Kakde, O. G. (2021). Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Computers in Biology and Medicine, 135, 104564. https://doi.org/10.1016/j.compbiomed.2021.104564
  • Thahira Banu, V., & Devi, M. R. (2021). Hybrid classifier to classify the finger nail abnormalities. Informatıon Technology In Industry, 9(1), 549-555. https://doi.org/10.17762/itii.v9i1.168
  • Theckedath, D., & Sedamkar, R. R. (2020). Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science, 1(2), 1-7.
  • Xiao, M., Zhang, L., Shi, W., Liu, J., He, W., & Jiang, Z. (2021, September). A visualization method based on the Grad-CAM for medical image segmentation model. In 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) (pp. 242-247). IEEE.
  • Yamaç, S. A., Kuyucuoğlu, O., Köseoğlu, Ş. B., & Ulukaya, S. (2022, July). Deep learning based classification of human nail diseases using color nail images. In 2022 45th International Conference on Telecommunications and Signal Processing (TSP) (pp. 196-199). IEEE.
  • Yani, M. (2019, May). Application of transfer learning using convolutional neural network method for early detection of terry’s nail. In Journal of Physics: Conference Series (Vol. 1201, No. 1, p. 012052). IOP Publishing.
There are 33 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Electrical and Electronics Engineering
Authors

Senar Ali Yamaç 0000-0003-0880-9202

Orhun Kuyucuoğlu 0000-0002-3415-6068

Şeyma Begüm Köseoğlu 0000-0001-7621-6850

Sezer Ulukaya 0000-0003-0473-7547

Publication Date March 15, 2023
Submission Date December 26, 2022
Published in Issue Year 2023Volume: 26 Issue: 1

Cite

APA Yamaç, S. A., Kuyucuoğlu, O., Köseoğlu, Ş. B., Ulukaya, S. (2023). DETECTION OF NAIL DISEASES USING ENSEMBLE MODEL BASED ON MAJORITY VOTING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 250-260. https://doi.org/10.17780/ksujes.1224006
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