Classification of skin cancer using VGGNet model structures

Volkan Kaya; İsmail Akgül

doi:10.17714/gumusfenbil.1069894

EN TR

Classification of skin cancer using VGGNet model structures

Abstract

Skin cancer is one of the most common type of cancer in humans. This type of cancer is produced by skin cells called melanocytes and occurs as a result of division and multiplication of the mentioned cells. The most important symptom of skin cancer is the formation of spots on the skin or the observation of changes in the shape, color, or size of the existing spot. It is necessary to consult a specialist to distinguish the difference between a normal spot and skin cancer. Expert physicians examine and follow up the spots on the skin using skin surface microscopy, called dermatoscopy, or take a sample from the suspicious area and request it to be examined in laboratory environment. This situation increases the cost of the procedure for the diagnosis of skin cancer and also causes it to be treated at a later stage. Therefore, there is a need for a metod that can detect skin cancer early. Thanks to machine learning, become popular in recent years, many diseases can be diagnosed with software that helps expert physicians. In this study, VGGNet model structures (VGG-11, VGG-13, VGG-16, VGG-19) that quickly classify skin cancer and become a traditional convolutional neural network architecture using deep learning method, a subfield of machine learning, were used. It has been observed that the VGG-11 architecture, which is one of the VGGNet model structures, detects skin cancer with superior success accuracy (83%) compared to other model structures.

Keywords

Deep learning , Machine learning , Skin cancer classification , VGGNet

VGGNet model yapıları kullanılarak cilt kanserinin sınıflandırılması

Öz

Cilt kanseri insanlarda en sık rastlanan kanser türlerinden birisidir. Bu kanser türü melanosit denilen cilt hücreleri tarafından üretilmekte ve bu hücrelerin bölünüp çoğalması sonucunda meydana gelmektedir. Cilt kanserinin en önemli belirtisi deri üzerinde leke oluşması veya var olan lekenin şeklinde, renginde veya büyüklüğündeki değişiklerin gözlenmesidir. Normal bir leke ile cilt kanserinin farkını ayırt etmek için uzman bir hekime başvurmak gereklidir. Uzman hekimler dermatoskopi olarak adlandırılan deri yüzeyi mikroskopisi kullanarak deri üzerindeki lekeleri incelerler ve takip altına alırlar veya şüpheli gördüğü bölgeden parça örneği alarak laboratuvar ortamında incelenmesini isterler. Bu durum cilt kanseri teşhisinin yapılabilmesi için hem işlem maliyetini artırmakta hem de daha geç evrede tedavi edilmesine yol açmaktadır. Bundan dolayı cilt kanserini erken teşhis edebilen bir yazılıma ihtiyaç duyulmaktadır. Son yıllarda popüler olan makine öğrenmesi sayesinde uzman hekimlere yardımcı olan bir yazılım ile birçok hastalık tanısı konulabilmektedir. Bu çalışmada makine öğrenmesinin bir alt alanı olan derin öğrenme yöntemi kullanılarak cilt kanserini hızlı bir şekilde sınıflandıran ve geleneksel bir evrişimsel sinir ağı mimarisi haline gelen VGGNet model yapıları (VGG-11, VGG-13, VGG-16, VGG-19) kullanılmıştır. VGGNet model yapılarından biri olan VGG-11 mimarisi diğer model yapılarına göre cilt kanserini daha üstün başarı doğruluğunda (%83) tespit ettiği gözlemlenmiştir.

Anahtar Kelimeler

Derin öğrenme , Makine öğrenmesi , Cilt kanseri sınıflandırma , VGGNet

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Volkan Kaya ^*
0000-0001-6940-3260
Türkiye

İsmail Akgül
0000-0003-2689-8675
Türkiye

Publication Date

January 15, 2023

Submission Date

February 8, 2022

Acceptance Date

December 8, 2022

Published in Issue

Year 2023 Volume: 13 Number: 1

DOI

https://doi.org/10.17714/gumusfenbil.1069894

IZ

https://izlik.org/JA56NU75XE

APA

Kaya, V., & Akgül, İ. (2023). Classification of skin cancer using VGGNet model structures. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(1), 190-198. https://doi.org/10.17714/gumusfenbil.1069894

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e-ISSN: 2146-538X Publication Model: Continuous Publication Citation Indexes: TR Dizin , SCOPUS