Skin Lesions Identification and Analysis with Deep Learning Model Using Transfer Learning

Year 2024, Volume: 7 Issue: 3, 1030 - 1045, 25.06.2024

Halit Çetiner

https://doi.org/10.47495/okufbed.1133801

Abstract

Sunlight has beneficial as well as harmful rays. Environmental pollution occurs as a result of the depletion of the ozone layer caused by the damage caused by humans to the environment. As a result of these pollutants, skin diseases can be seen in areas exposed to direct sunlight, such as the head and neck. Early detection of actinic keratosis (akiec), basal cell carcinoma (bcc), bening keratosis (bkl), dermafibroma (df), melanoma (mel), melanocytic nevi (nv), and vascular (vasc) skin cancer types, which is one of the most common skin diseases, is important for medical intervention. Otherwise, severe spread, called metastasis, may occur as a result of aggressive growths. For the stated reasons, a deep learning model based on transfer learning, which can classify skin cancer types, has been proposed to assist the medical personnel who serve in this field. With this proposed model, the aim is to classify at high accuracy rates without any pre-processing. As a result of the experimental studies carried out as a result of the stated goals, an accuracy rate of 99,51% was achieved with the proposed model.

Keywords

Skin cancer, Deep learning, CNN, DenseNet201

Transfer Öğrenme Kullanan Derin Öğrenme Modeli ile Cilt Lezyonlarının Tanımlanması ve Analizi

Abstract

Güneş ışıklarından faydalı ışınları olduğu gibi zararlı ışınları da bulunmaktadır. İnsanların çevreye verdikleri zararlar ile oluşan ozon tabakası incelmeleri sonucunda çevresel kirlilik meydana gelmektedir. Bu kirlilikler neticesinde de baş ve boyun gibi doğrudan güneş ışığına maruz kalan bölgelerde cilt hastalıkları görülebilmektedir. En sık olarak görülen cilt hastalıklarından olan actinic keratosis (akiec), basal cell carcinoma (bcc), bening keratosis (bkl), dermafibroma (df), melanoma (mel), melanocytic nevi (nv), ve vascular (vasc) cilt kanseri türlerinin erken aşamada tespit edilmesi tıbbi müdahale açısından önemlidir. Aksi takdirde agresif büyümeler sonucunda metastaz adı verilen şiddetli yayılmalar meydana gelebilmektedir. Belirtilen sebeplerden dolayı bu alanda hizmet veren uzman sağlık personeline yardımcı cilt kanser türlerini sınıflandırabilen transfer öğrenme tabanlı derin öğrenme modeli önerilmiştir. Önerilen bu model ile herhangi bir ön işleme tabi tutmadan yüksek doğruluk oranlarında sınıflandırma yapmak hedeflenmiştir. Belirtilen hedefler sonucunda yapılan deneysel çalışmalar neticesinde önerilen model ile %99.51 oranında başarı oranına ulaşılmıştır.

Keywords

Deri kanseri, Derin öğrenme, CNN, DenseNet201

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