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Classification of Skin Cancer with Deep Transfer Learning Method

Yıl 2022, Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 202 - 210, 10.10.2022
https://doi.org/10.53070/bbd.1172782

Öz

Skin cancer is a serious health hazard for human society. This disease is developed when the pigments that produce skin color become cancerous. Dermatologists face difficulties in diagnosing skin cancer since many skin cancer colors seem identical. As a result, early diagnosis of lesions (the foundation of skin cancer) is very crucial and beneficial in totally curing skin cancer patients. Significant progress has been made in creating automated methods with the development of artificial intelligence (AI) technologies to aid dermatologists in the identification of skin cancer. The widespread acceptance of AI-powered technologies has enabled the use of a massive collection of photos of lesions and benign sores authorized by histology. This research compares six alternative transfer learning networks (deep networks) for skin cancer classification using the International Skin Imaging Collaboration (ISIC) dataset. DenseNet, Xception, InceptionResNetV2, ResNet50, and MobileNet were the transfer learning networks employed in the investigation which were successful in different studies recently. To compensate for the imbalance in the ISIC dataset, the photos of classes with low frequencies are augmented. The results show that augmentation is appropriate for the classification success, with high classification accuracies and F-scores with decreased false negatives. With an accuracy rate of 98.35%, modified DenseNet121 was the most successful model against the rest of the transfer learning nets utilized in the study.

Teşekkür

Bu çalışma "6th International Artificial Intelligence and Data Processing Symposium"da bildiri olarak sunulmuştur.

Kaynakça

  • Ali, A. A., & Al-Marzouqi, H. (2017). Melanoma detection using regular convolutional neural networks. 2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2018-January, 1–5. https://doi.org/10.1109/ICECTA.2017.8252041
  • 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
  • Ayoub, A., Mahboob, K., Javed, A. R., Rizwan, M., Gadekallu, T. R., Abidi, M. H., & Alkahtani, M. (2021). Classification and categorization of COVID-19 outbreak in Pakistan. Computers, Materials and Continua, 69(1), 1253–1269. https://doi.org/10.32604/CMC.2021.015655
  • Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 1800–1807. https://doi.org/10.48550/arxiv.1610.02357
  • Dabhi, V. M., Kashyap, S. S., Nithin, G., Vamshi, A. C., & Krishna, G. A. (2021). Detection and Classification of Skin Cancer using Back Propagated Artificial Neural Networks. JES - Journal of Engineering Sciences, 12(06), 686–693.
  • Demir, A., Yilmaz, F., & Kose, O. (2019). Early detection of skin cancer using deep learning architectures: Resnet-101 and inception-v3. TIPTEKNO 2019 - Tip Teknolojileri Kongresi, 2019-January. https://doi.org/10.1109/TIPTEKNO47231.2019.8972045
  • Fu, Z., An, J., Yang, Q., Yuan, H., Sun, Y., & Ebrahimian, H. (2022). Skin cancer detection using Kernel Fuzzy C-means and Developed Red Fox Optimization algorithm. Biomedical Signal Processing and Control, 71, 103160. https://doi.org/10.1016/J.BSPC.2021.103160
  • Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., ben Hadj Hassen, A., Thomas, L., Enk, A., Uhlmann, L., Alt, C., Arenbergerova, M., Bakos, R., Baltzer, A., Bertlich, I., Blum, A., Bokor-Billmann, T., Bowling, J., … Zalaudek, I. (2018). Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 29(8), 1836–1842. https://doi.org/10.1093/ANNONC/MDY166
  • Harangi, B., Baran, A., & Hajdu, A. (2018). Classification of Skin Lesions Using An Ensemble of Deep Neural Networks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 2575–2578. https://doi.org/10.1109/EMBC.2018.8512800
  • Hasan, M., Barman, S. das, Islam, S., & Reza, A. W. (2019). Skin cancer detection using convolutional neural network. ACM International Conference Proceeding Series, 254–258. https://doi.org/10.1145/3330482.3330525
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.48550/arxiv.1512.03385
  • Hosny, K. M., Kassem, M. A., & Foaud, M. M. (2019). Skin Cancer Classification using Deep Learning and Transfer Learning. 2018 9th Cairo International Biomedical Engineering Conference, CIBEC 2018 - Proceedings, 90–93. https://doi.org/10.1109/CIBEC.2018.8641762
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv. https://doi.org/10.48550/arxiv.1704.04861
  • ISIC. (2022). ISIC Archive. The International Skin Imaging Collaboration. https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main
  • Kalouche, S. (2016). Vision-Based Classification of Skin Cancer using Deep Learning. Semantic Scholar. https://www.semanticscholar.org/paper/Vision-Based-Classification-of-Skin-Cancer-using-Kalouche/b57ba909756462d812dc20fca157b3972bc1f533
  • Namozov, A., Ergashev, D., & Cho, Y. I. (2018). Adaptive activation functions for skin lesion classification using deep neural networks. Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018, 232–235. https://doi.org/10.1109/SCIS-ISIS.2018.00048
  • Nugroho, A. A., Slamet, I., & Sugiyanto. (2019). Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network. AIP Conference Proceedings, 2202(1), 020039. https://doi.org/10.1063/1.5141652
  • Qiao, L., Xue, Y., Tang, W., & Jimenez, G. (2022). Skin cancer diagnosis based on a hybrid AlexNet/extreme learning machine optimized by Fractional-order Red Fox Optimization algorithm. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine. https://doi.org/10.1177/09544119221075941
  • Rezvantalab, A., Safigholi, H., & Karimijeshni, S. (2018). Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. ArXiv Preprint ArXiv:1810.10348.
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. https://doi.org/10.1007/s13369-021-06131-3
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2019). Performance Comparison of Carotid Artery Intima Media Thickness Classification by Deep Learning Methods. International Congress on Human-Computer Interaction, Optimization and Robotic Applications Proceedings, 4(5), 125–131. https://doi.org/10.36287/setsci.4.5.025
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2022). Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet. Bilişim Teknolojileri Dergisi, 15(1), 1–12.
  • Senan, E. M., & Jadhav, M. E. (2021). Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer. Global Transitions Proceedings, 2(1), 1–7. https://doi.org/10.1016/J.GLTP.2021.01.001
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 4278–4284. https://doi.org/10.48550/arxiv.1602.07261
  • Tan, M., & Le, Q. v. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
  • 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
  • Wang, Y., Louie, D. C., Cai, J., Tchvialeva, L., Lui, H., Jane Wang, Z., & Lee, T. K. (2021). Deep learning enhances polarization speckle for in vivo skin cancer detection. Optics & Laser Technology, 140, 107006. https://doi.org/10.1016/J.OPTLASTEC.2021.107006
  • WCRF. (2022). Skin cancer statistics. World Cancer Research Fund International. https://www.wcrf.org/cancer-trends/skin-cancer-statistics/
  • WHO. (2017). Radiation: Ultraviolet (UV) radiation and skin cancer. World Health Organization. https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer

Classification of Skin Cancer with Deep Transfer Learning Method

Yıl 2022, Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 202 - 210, 10.10.2022
https://doi.org/10.53070/bbd.1172782

Öz

Skin cancer is a serious health hazard for human society. This disease is developed when the pigments that produce skin color become cancerous. Dermatologists face difficulties in diagnosing skin cancer since many skin cancer colors seem identical. As a result, early diagnosis of lesions (the foundation of skin cancer) is very crucial and beneficial in totally curing skin cancer patients. Significant progress has been made in creating automated methods with the development of artificial intelligence (AI) technologies to aid dermatologists in the identification of skin cancer. The widespread acceptance of AI-powered technologies has enabled the use of a massive collection of photos of lesions and benign sores authorized by histology. This research compares six alternative transfer learning networks (deep networks) for skin cancer classification using the International Skin Imaging Collaboration (ISIC) dataset. DenseNet, Xception, InceptionResNetV2, ResNet50, and MobileNet were the transfer learning networks employed in the investigation which were successful in different studies recently. To compensate for the imbalance in the ISIC dataset, the photos of classes with low frequencies are augmented. The results show that augmentation is appropriate for the classification success, with high classification accuracies and F-scores with decreased false negatives. With an accuracy rate of 98.35%, modified DenseNet121 was the most successful model against the rest of the transfer learning nets utilized in the study.

Kaynakça

  • Ali, A. A., & Al-Marzouqi, H. (2017). Melanoma detection using regular convolutional neural networks. 2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2018-January, 1–5. https://doi.org/10.1109/ICECTA.2017.8252041
  • 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
  • Ayoub, A., Mahboob, K., Javed, A. R., Rizwan, M., Gadekallu, T. R., Abidi, M. H., & Alkahtani, M. (2021). Classification and categorization of COVID-19 outbreak in Pakistan. Computers, Materials and Continua, 69(1), 1253–1269. https://doi.org/10.32604/CMC.2021.015655
  • Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 1800–1807. https://doi.org/10.48550/arxiv.1610.02357
  • Dabhi, V. M., Kashyap, S. S., Nithin, G., Vamshi, A. C., & Krishna, G. A. (2021). Detection and Classification of Skin Cancer using Back Propagated Artificial Neural Networks. JES - Journal of Engineering Sciences, 12(06), 686–693.
  • Demir, A., Yilmaz, F., & Kose, O. (2019). Early detection of skin cancer using deep learning architectures: Resnet-101 and inception-v3. TIPTEKNO 2019 - Tip Teknolojileri Kongresi, 2019-January. https://doi.org/10.1109/TIPTEKNO47231.2019.8972045
  • Fu, Z., An, J., Yang, Q., Yuan, H., Sun, Y., & Ebrahimian, H. (2022). Skin cancer detection using Kernel Fuzzy C-means and Developed Red Fox Optimization algorithm. Biomedical Signal Processing and Control, 71, 103160. https://doi.org/10.1016/J.BSPC.2021.103160
  • Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., ben Hadj Hassen, A., Thomas, L., Enk, A., Uhlmann, L., Alt, C., Arenbergerova, M., Bakos, R., Baltzer, A., Bertlich, I., Blum, A., Bokor-Billmann, T., Bowling, J., … Zalaudek, I. (2018). Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 29(8), 1836–1842. https://doi.org/10.1093/ANNONC/MDY166
  • Harangi, B., Baran, A., & Hajdu, A. (2018). Classification of Skin Lesions Using An Ensemble of Deep Neural Networks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 2575–2578. https://doi.org/10.1109/EMBC.2018.8512800
  • Hasan, M., Barman, S. das, Islam, S., & Reza, A. W. (2019). Skin cancer detection using convolutional neural network. ACM International Conference Proceeding Series, 254–258. https://doi.org/10.1145/3330482.3330525
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.48550/arxiv.1512.03385
  • Hosny, K. M., Kassem, M. A., & Foaud, M. M. (2019). Skin Cancer Classification using Deep Learning and Transfer Learning. 2018 9th Cairo International Biomedical Engineering Conference, CIBEC 2018 - Proceedings, 90–93. https://doi.org/10.1109/CIBEC.2018.8641762
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv. https://doi.org/10.48550/arxiv.1704.04861
  • ISIC. (2022). ISIC Archive. The International Skin Imaging Collaboration. https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main
  • Kalouche, S. (2016). Vision-Based Classification of Skin Cancer using Deep Learning. Semantic Scholar. https://www.semanticscholar.org/paper/Vision-Based-Classification-of-Skin-Cancer-using-Kalouche/b57ba909756462d812dc20fca157b3972bc1f533
  • Namozov, A., Ergashev, D., & Cho, Y. I. (2018). Adaptive activation functions for skin lesion classification using deep neural networks. Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018, 232–235. https://doi.org/10.1109/SCIS-ISIS.2018.00048
  • Nugroho, A. A., Slamet, I., & Sugiyanto. (2019). Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network. AIP Conference Proceedings, 2202(1), 020039. https://doi.org/10.1063/1.5141652
  • Qiao, L., Xue, Y., Tang, W., & Jimenez, G. (2022). Skin cancer diagnosis based on a hybrid AlexNet/extreme learning machine optimized by Fractional-order Red Fox Optimization algorithm. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine. https://doi.org/10.1177/09544119221075941
  • Rezvantalab, A., Safigholi, H., & Karimijeshni, S. (2018). Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. ArXiv Preprint ArXiv:1810.10348.
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. https://doi.org/10.1007/s13369-021-06131-3
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2019). Performance Comparison of Carotid Artery Intima Media Thickness Classification by Deep Learning Methods. International Congress on Human-Computer Interaction, Optimization and Robotic Applications Proceedings, 4(5), 125–131. https://doi.org/10.36287/setsci.4.5.025
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2022). Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet. Bilişim Teknolojileri Dergisi, 15(1), 1–12.
  • Senan, E. M., & Jadhav, M. E. (2021). Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer. Global Transitions Proceedings, 2(1), 1–7. https://doi.org/10.1016/J.GLTP.2021.01.001
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 4278–4284. https://doi.org/10.48550/arxiv.1602.07261
  • Tan, M., & Le, Q. v. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
  • 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
  • Wang, Y., Louie, D. C., Cai, J., Tchvialeva, L., Lui, H., Jane Wang, Z., & Lee, T. K. (2021). Deep learning enhances polarization speckle for in vivo skin cancer detection. Optics & Laser Technology, 140, 107006. https://doi.org/10.1016/J.OPTLASTEC.2021.107006
  • WCRF. (2022). Skin cancer statistics. World Cancer Research Fund International. https://www.wcrf.org/cancer-trends/skin-cancer-statistics/
  • WHO. (2017). Radiation: Ultraviolet (UV) radiation and skin cancer. World Health Organization. https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm PAPERS
Yazarlar

Doaa Khalid Abdulridha Al-saedi Bu kişi benim 0000-0001-9463-4052

Serkan Savaş 0000-0003-3440-6271

Yayımlanma Tarihi 10 Ekim 2022
Gönderilme Tarihi 8 Eylül 2022
Kabul Tarihi 16 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium

Kaynak Göster

APA Al-saedi, D. K. A., & Savaş, S. (2022). Classification of Skin Cancer with Deep Transfer Learning Method. Computer Science, IDAP-2022 : International Artificial Intelligence and Data Processing Symposium, 202-210. https://doi.org/10.53070/bbd.1172782

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