Presented at the 6th International Symposium on Innovative Approaches in Smart Technologies (ISAS-WINTER-2022), Online, Turkey, Dec 08, 2022

SETSCI Conference Proceedings, 2022, 14, Page (s): 38-40 , https://doi.org/10.36287/setsci.5.2.008

Published Date: 22 December 2022    | 1453     15

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. Melanoma is most dangerous type of skin cancer. Basal cell carcinoma is type with high cancer potential. Properly classified images can help doctors predict the type of skin cancer.  This study aims to classify into three classes, including the normal type.

Keywords - Deep learning, skin cancer, image classification, ResNet, LinkNet

[1] M. Sucu, “Karar Destek Sistemleri ve İş Zekası Uygulamalarının İşletmeler Açısından Önemi: Bir Literatür Araştırması,” Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 44, pp. 261-283, 2020.

[2] M. Dönerçark, ve V. Tecim, “Kurumsal Karar Destek Sistemlerinde Yapay Zekâ Kullanımı: Tasarım ve Uygulama,” Yönetim Bilişim Sistemleri Dergisi, vol. 6, no. 2, pp. 77-103, 2020.

[3] M. Özata, ve Ş. Aslan, “Klinik Karar Destek Sistemleri ve Örnek Uygulamalar,” Kocatepe Tıp Dergisi, vol. 5, pp. 11-17, 2004.

[4] P. Thapar, M. Rakhra, G. Cazzato, and S. Hossain, “A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification,” Hindawi Journal of Healthcare Engineering, vol. 2022, pp. 1-21, 2022.

[5] R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, “Cancer statistics,” CA: A Cancer Journal of Clinicians, vol. 71, no. 1, pp. 7-33, 2021.

[6] H. M. Ünver, and E. Ayan, “Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm,” Diagnostics Journal, vol. 9, no. 3, pp. 1-21, 2019.

[7] R. Kaur, H: Gholamhosseini, R. Sinha, and M. Lindén, “Melanom Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images,” Sensors, vol. 22, no. 3, pp. 1-15, 2022.

[8] A. Suresh, and R. Seeja, “Deep Learning Based Skin Lesion Segmentation and Classification of Melanom Using Support Vector Machine (SVM),” Asian Pacific Journal of Cancer Prevention, vol. 20, no. 5, pp. 1555-1561, 2019.

[9] R. Indraswari, R. Rokhana, and W. Herulambang, “Melanom image classification based on MobileNetV2 network,” Procedia Computer Science, vol. 197, pp. 198–207, 2019.

[10] Ali, S., Islam, K., Haque, J., Miah, S., and Rahman, M. (2021). An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Machine Learning with Applications, 5, 1-8.

[11] N. Kausar, A. Hameed, M. Sattar, R. Ashraf, A. S. Imran, M. Z. Abidin, and A. Ali, “Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models,”. Applied Sciences, vol. 11, no. 1-20, 2021.

[12] M. A. Khan, T. Akram, M. Sharif, S. Kadry, and Y. Nam, “Computer Decision Support System for Skin Cancer Localization and Classification,” Computers, Materials & Continua, vol. 68, no. 1, pp. 1043-1064, 2021.

[13] Ö. Polat, Ö., and M. S. Kartal, “Detection of Benign and Malignant Skin Cancer from Dermoscopic Images using Modified Deep Residual Learning Model,” AITA Journal, vol. 2, no. 2, pp. 10-18, 2022.

[14] Y. Akyel, C. Görüntü İşleme ve Derin Öğrenme Yöntemleri ile Cilt Kanseri Teşhisi için Karar Destek Sisteminin Geliştirilmesi, Phd Thesis, Gazi Üniversitesi, Yönetim Bilişim Sistemleri, 2022