MONKEYPOX DISEASE DETECTION FROM SKIN LESION IMAGES USING TRANSFER LEARNING

Year 2024, , 148 - 164, 30.04.2024

Hüseyin Fırat , Hüseyin Üzen

https://doi.org/10.54365/adyumbd.1411927

Abstract

Monkeypox is a viral disease predominantly found in Central and West Africa, resulting from infection with the monkeypox virus. Its transmission occurs through close contact with infected individuals, manifesting as flu-like symptoms and skin rashes, often resembling chickenpox or measles, thus increasing the risk of misdiagnosis. Timely and precise diagnosis is crucial for effective medical intervention. Recently, deep learning-based transfer learning methods have emerged as a promising means to accurately differentiate monkeypox from similar diseases. This study leverages pre-trained convolutional neural networks, including VGG16, ResNet models, Xception, Inception models, DenseNet121, and DenseNet201, to create robust diagnostic models by extracting pertinent features from medical images. The "Monkeypox Skin Lesion Dataset" on Kaggle, comprising two classes (Monkeypox and others), was employed to assess these models. Experimental findings revealed that the DenseNet201 model achieved the highest classification accuracy, reaching 95.56%, highlighting its effectiveness when compared to existing literature.

Keywords

Deep learning, Monkeypox disease, Transfer learning, Classification

TRANSFER ÖĞRENME KULLANILARAK DERİ LEZYON GÖRÜNTÜLERİNDEN MAYMUN ÇİÇEĞİ HASTALIĞININ TESPİTİ

Abstract

Maymun çiçeği, ağırlıklı olarak Orta ve Batı Afrika'da bulunan ve maymun çiçeği virüsü enfeksiyonundan kaynaklanan viral bir hastalıktır. Bulaşma, enfeksiyon kapmış kişilerle yakın temas yoluyla meydana gelmektedir. Grip benzeri semptomlar ve deri döküntüleri şeklinde kendini göstermektedir. Çoğunlukla su çiçeği veya kızamığa benzer ve dolayısıyla yanlış teşhis riskini arttırmaktadır. Etkili tıbbi müdahale için zamanında ve kesin tanı çok önemlidir. Son zamanlarda, derin öğrenmeye dayalı transfer öğrenme yöntemleri, maymun çiçeğini benzer hastalıklardan doğru bir şekilde ayırt etmek için umut verici bir araç olarak ortaya çıkmıştır. Bu çalışma, tıbbi görüntülerden ilgili özellikleri çıkararak sağlam teşhis modelleri oluşturmak için VGG16, ResNet modelleri, Xception, Inception modelleri, DenseNet121 ve DenseNet201 dahil olmak üzere önceden eğitilmiş evrişimsel sinir ağlarından yararlanmaktadır. Bu modelleri değerlendirmek için Kaggle'daki iki sınıftan (MaymunÇiçeği ve diğerleri) oluşan "Maymun Çiçeği Cilt Lezyonu Veri Seti" kullanılmıştır. Deneysel bulgular, DenseNet201 modelinin %95.56'ya ulaşarak en yüksek sınıflandırma doğruluğuna ulaştığını ve mevcut literatürle karşılaştırıldığında etkinliğini öne çıkardığını ortaya koymaktadır.

Keywords

Derin öğrenme, Maymun çiçeği hastalığı, Transfer öğrenimi, Sınıflandırma

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