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

DETECTION OF NAIL DISEASES USING ENSEMBLE MODEL BASED ON MAJORITY VOTING

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

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