U-Net-RCB7: Image Segmentation Algorithm

Year 2023, Volume: 26 Issue: 4, 1555 - 1562, 01.12.2023

Cihan Akyel , Nursal Arıcı

https://doi.org/10.2339/politeknik.1208936

Cited By: 2

Abstract

The incidence of skin cancer is increasing. Early detection of cases of skin cancer is vital for treatment. Recently, computerized methods have been widely used in cancer diagnosis. These methods have important advantages such as no human error, short diagnosis time, and low cost. We can segment skin cancer images using deep learning and image processing. Properly segmented images can help doctors predict the type of skin cancer. However, skin images can contain noise such as hair. These noises affect the accuracy of segmentation. In our study, we created a noise dataset. It contains 3000 images and masks. We performed noise removal and lesion segmentation by utilizing the ISIC and PH2. We have developed a new deep learning model called U-Net-RCB7. U-Net-RCB7 contains EfficientNetB7 as the encoder and ResNetC before the last layer. This paper uses a modified U-Net model. Images were divided into 36 layers to prevent loss of pixel values in the images. As a result, noise removal and lesion segmentation were 96% and 98.36% successful, respectively.

Keywords

deep learning, skin cancer, devision support systems, image proccessing, UNet

U-Net-RCB7: Görüntü Bölütleme Algoritması

Abstract

Cilt kanseri insidansı artmaktadır. Cilt kanseri vakalarının erken tespiti tedavi için hayati önem taşır. Son zamanlarda kanser teşhisinde bilgisayarlı yöntemler yaygın olarak kullanılmaktadır. Bu yöntemlerin insan hatası olmaması, kısa teşhis süresi ve düşük maliyet gibi önemli avantajları vardır. Derin öğrenme ve görüntü işlemeyi kullanarak cilt kanseri görüntülerini segmentlere ayırabiliriz. Düzgün şekilde bölümlere ayrılmış görüntüler, doktorların cilt kanseri türünü tahmin etmesine yardımcı olabilir. Bununla birlikte, cilt görüntüleri saç gibi gürültüler içerebilir. Bu sesler, segmentasyonun doğruluğunu etkiler. Çalışmamızda bir gürültü veri seti oluşturduk. 3000 resim ve maske içerir. ISIC ve PH2'yi kullanarak gürültü giderme ve lezyon segmentasyonu gerçekleştirdik. U-Net-RCB7 adlı yeni bir derin öğrenme modeli geliştirdik. U-Net-RCB7, kodlayıcı olarak EfficientNetB7'yi ve son katmandan önce ResNetC'yi içerir. Bu yazıda değiştirilmiş bir U-Net modeli kullanılmaktadır. Görüntülerde piksel değerlerinin kaybolmaması için görüntüler 36 katmana ayrılmıştır. Sonuç olarak, gürültü giderme ve lezyon segmentasyonu sırasıyla %96 ve %98.36 başarılı olmuştur.

Keywords

derin öğrenme, cilt kanseri, karar destekleme sistemleri, UNet

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