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

Halit Çetiner

Makale detayları

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

Yazarlar:

Halit Çetiner

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.7 Issue 3, pp.1030-1045

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320293

pISSN:

2687-3729

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Halit Çetiner

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.7 Issue 3, pp.1030-1045

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320293

pISSN:

2687-3729

Skin cancer

Deep learning

CNN

DenseNet201Deri kanseri

Derin öğrenme

CNN

DenseNet201

Sayı

Makale detayları

Yazarlar: Halit Çetiner

Yayıncı: Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.7 Issue 3, pp.1030-1045

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001320293

pISSN: 2687-3729

Sayı

Yazarlar:

Halit Çetiner

Yayıncı:

Osmaniye Korkut Ata Üniversitesi

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.7 Issue 3, pp.1030-1045

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320293

pISSN:

2687-3729