Cilt Kanseri Görüntülerinde Gürültü Temizliği ve Lezyonun Dört Sınıfa Ayrılması

Year 2024, Volume: 24 Issue: 2, 284 - 293, 29.04.2024

Cihan Akyel , Nursal Arıcı

https://doi.org/10.35414/akufemubid.1211510

Abstract

Günümüzde cilt kanseri çevresel koşulların da etkisiyle artış göstermektedir. Cilt kanserinin birçok farklı türü olmasına rağmen melanom (MEL) kötü huylu ve en ölümcül olanıdır. Bazal hücre karsinomu (BHK) ve skuamöz hücre karsinomu (SHK) cilt kanserleri de diğer organlara yayılım eğilimi gösterebilmektedirler. Cilt kanserinde erken teşhis tedavi sürecinde çok önemlidir. Cilt kanseri renk geçişleri, yapısal durumu gibi özelliklere bakılarak sınıflandırılabilmektedir. Kanser teşhisinde derin öğrenme ve görüntü işleme algoritmalarının kullanımı yüksek başarı oranı ve insan hatasını bertaraf etmesinden dolayı kullanımı yaygınlaşmaktadır. Lezyon görüntülerinde bulunan kıl, mürekkep izi gibi gürültüler lezyonun bu yöntemlerle sınıflandırılmasında başarıyı düşürmektedir. Çalışmada LinkNetRCB7 modeli ve görüntü işleme algoritmaları ile lezyon görüntülerinde gürültü temizliği yapılmıştır. Bu aşamada %97 eğitim başarısı elde edilmiştir. Sınıflama aşamasında çalışmada BHK, SHK, MEL ve iyi huylu olmak üzere görüntüler ISIC 2019’a ait veri seti ile dört sınıfa ayrılmıştır. Bu aşamada %94.87 eğitim başarısı gözlemlenmiştir.

Keywords

Derin öğrenme, Cilt kanseri, Sınıflama, LinkNet, ResNet, Classification

Noise Removal in Skin Cancer Images and Classification of Lesions into Four Classes

Abstract

Today, skin cancer is increasing with the effect of environmental conditions. Although there are many different types of skin cancer, melanoma (MEL) is the most malignant and the most deadly. Basal cell carcinoma (BCC) and squamous cell carcinoma (SHC) skin cancers may also tend to spread to other organs. Early diagnosis of skin cancer is very important in the treatment process. Skin cancer can be classified by looking at features such as color transitions and structural status. The use of deep learning and image processing algorithms in cancer diagnosis is becoming widespread due to its high success rate and elimination of human error. Noises such as hair and ink traces in the lesion images reduce the success in classifying the lesion with these methods. In the study, noise cleaning was performed on lesion images with the LinkNetRCB7 model and image processing algorithms. At this stage, 97% educational success was achieved. In the classification phase, the images were divided into four classes with the data set of ISIC 2019: BHK, SHK, MEL and benign. At this stage, 94.87% educational success was observed.

Keywords

Deep learning, Skin cancer, Classification, LinkNet, ResNet

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