Classification of skin cancer using VGGNet model structures

Year 2023, Volume: 13 Issue: 1, 190 - 198, 15.01.2023

Volkan Kaya , İsmail Akgül

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

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

Abstract

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.

Keywords

Derin öğrenme, Makine öğrenmesi, Cilt kanseri sınıflandırma, VGGNet

References

• Abuared, N., Panthakkan, A., Al-Saad, M., Amin, S. A., & Mansoor, W. (2020). Skin cancer classification model based on VGG 19 and transfer learning. In 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS) (pp. 1-4), Dubai. https://doi.org/10.1109/ICSPIS51252.2020.9340143
• Agarwal, K., & Singh, T. (2022). Classification of skin cancer ımages using convolutional neural networks, arXiv preprint. https://doi.org/10.48550/arXiv.2202.00678
• Akyel, C., & Arıcı, N. (2020). Cilt kanserinde kıl temizliği ve lezyon bölütlemesinde yeni bir yaklaşım. Politeknik Dergisi, 23(3), 821-828. https://doi.org/10.2339/politeknik.645395
• Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET), (pp. 1-6), Antalya. https://doi.org/10.1109/ICEngTechnol.2017.8308186
• Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. (2021). An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Machine Learning with Applications, 5, 100036. https://doi.org/10.1016/j.mlwa.2021.100036
• Ashraf, R., Afzal, S., Rehman, A. U., Gul, S., Baber, J., Bakhtyar, M., Mehmood, I., Song, O. Y., & Maqsood, M. (2020). Region-of-Interest based transfer learning assisted framework for skin cancer detection. IEEE Access, 8, 147858-147871. https://doi.org/10.1109/ACCESS.2020.3014701
• Dascalu, A., & David, E. O. (2019). Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope. EBioMedicine, 43, 107-113. https://doi.org/10.1016/j.ebiom.2019.04.055
• Demir, A., Yilmaz, F., & Kose, O. (2019). Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3. In 2019 Medical Technologies Congress (TIPTEKNO) (pp. 1-4), İzmir. https://doi.org/10.1109/TIPTEKNO47231.2019.8972045
• Demir, F. (2021). Derin öğrenme tabanlı yaklaşımla kötü huylu deri kanserinin dermatoskopik görüntülerden saptanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 617-624. https://doi.org/10.35234/fumbd.900170
• Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin cancer detection: A review using deep learning techniques. International journal of Environmental Research and Public Health, 18(10), 5479. https://doi.org/10.3390/ijerph18105479
• Ergün, E., & Kılıç, K. (2021). Derin öğrenme ile artırılmış görüntü seti üzerinden cilt kanseri tespiti. Black Sea Journal of Engineering and Science, 4(4), 192-200. https://doi.org/10.34248/bsengineering.938520
• Göreke, V. (2021). Cilt lezyonlarının sınıflandırılmasında derin öğrenme tabanlı bir yöntem. Türk Doğa ve Fen Dergisi, 10(1), 30-36. https://doi.org/10.46810/tdfd.797683
• Hasan, M. R., Fatemi, M. I., Monirujjaman Khan, M., Kaur, M., & Zaguia, A. (2021). Comparative analysis of skin cancer (benign vs. malignant) detection using convolutional neural networks. Journal of Healthcare Engineering, 2021, 1-17. https://doi.org/10.1155/2021/5895156
• Hosny, K. M., Kassem, M. A., & Foaud, M. M. (2018). Skin cancer classification using deep learning and transfer learning. In 2018 9th Cairo International Biomedical Engineering Conference (CIBEC) (pp. 90-93), Cairo. https://doi.org/10.1109/CIBEC.2018.8641762
• Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020). The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules, 10(8), 1123. https://doi.org/10.3390/biom10081123
• Kadampur, M. A., & Al Riyaee, S. (2020). Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 18, 100282. https://doi.org/10.1016/j.imu.2019.100282
• Kaggle. (2021, December, 12). https://www.kaggle.com/fanconic/skin-cancer-malignant-vs-benign
• Kaya, V., Baran, A., & Tuncer, S. (2021a). Dinamit destekli terör faaliyetlerinin önlenmesi için derin öğrenme temelli güvenlik destek sistemi. Avrupa Bilim ve Teknoloji Dergisi, 22, 81-85. https://doi.org/10.31590/ejosat.845467
• Kaya, V., Tuncer, S., & Baran, A. (2021b). Detection and classification of different weapon types using deep learning. Applied Sciences, 11(16), 7535. https://doi.org/10.3390/app11167535
• Kaya, V., Tuncer. S., Baran, A. (2020). Derı̇n öğrenme yöntemlerı̇ kullanılarak nesne tanıma. International Science and Technology Conference (ISTEC) (pp. 277-287), Kıbrıs.
• Khamparia, A., Singh, P. K., Rani, P., Samanta, D., Khanna, A., & Bhushan, B. (2021). An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. Transactions on Emerging Telecommunications Technologies, 32(7), e3963. https://doi.org/10.1002/ett.3963
• Li, P., Wang, D., Wang, L., Lu, H. (2018). Deep visual tracking: Review and experimental comparison. Pattern Recognition, 76, 323-338. https://doi.org/10.1016/j.patcog.2017.11.007
• Manasa, K., & Murthy, D. G. V. (2021). Skin cancer detection using VGG-16. European Journal of Molecular & Clinical Medicine, 8(1), 1419-1426.
• Manne, R., Kantheti, S., & Kantheti, S. (2020). Classification of skin cancer using deep learning, convolutional neural networks-opportunities and vulnerabilities-a systematic review. International Journal for Modern Trends in Science and Technology, 6(11), 101-108. https://doi.org/10.46501/IJMTST061118
• Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A., Iqbal, M., & Saba, T. (2021). Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering. Microscopy research and technique, 85(1), 339-351. https://doi.org/10.1002/jemt.23908
• Pathak, A. R., Pandey, M., Rautaray, S. (2018). Application of deep learning for object detection. Procedia Computer Science, 132, 1706-1717. https://doi.org/10.1016/j.procs.2018.05.144
• Saba, T. (2021). Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features. Microscopy Research and Technique, 84(6), 1272-1283. https://doi.org/10.1002/jemt.23686
• Shorfuzzaman, M. (2021). An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimedia Systems, 28(4), 1309-1323. https://doi.org/10.1007/s00530-021-00787-5
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint. https://doi.org/10.48550/arXiv.1409.1556
• Soylu, E., & Demir, R. (2021). Development and comparison of skin cancer diagnosis models. Avrupa Bilim ve Teknoloji Dergisi, 28, 1217-1221. https://doi.org/10.31590/ejosat.1013910
• Thomas, S. M., Lefevre, J. G., Baxter, G., & Hamilton, N. A. (2021). Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Medical Image Analysis, 68, 101915. https://doi.org/10.1016/j.media.2020.101915
• Toğaçar, M., Cömert, Z., & Ergen, B. (2021). Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks. Chaos, Solitons & Fractals, 144, 110714. https://doi.org/10.1016/j.chaos.2021.110714
• Tumpa, P. P., & Kabir, M. A. (2021). An artificial neural network based detection and classification of melanoma skin cancer using hybrid texture features. Sensors International, 2, 100128. https://doi.org/10.1016/j.sintl.2021.100128
• Yıldız, O. (2019). Melanoma detection from dermoscopy images with deep learning methods: A comprehensive study. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 2241-2260. https://doi.org/10.17341/gazimmfd.435217