Research Article
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An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures

Year 2024, Volume: 7 Issue: 3, 449 - 459, 31.12.2024

Gökçen Çetinel , Bekir Murat Aydın , Sevda Gül , Devrim Akgün , Rabia Öztaş Kara

https://doi.org/10.35377/saucis...1543993

Abstract

Skin lesion segmentation for recognizing and defining the boundaries of skin lesions in images is proper for automated analysis of skin lesion images, especially for the early diagnosis and detection of skin cancers. Deep learning architectures are an efficient way to implement segmentation once a skin lesion dataset is provided with ground truth images. This study evaluates deep learning architectures on a hybrid dataset, including a private dataset collected from a hospital and a public ISIC dataset. Four different test cases exist in the analysis where the combinations of public and private datasets are used as train and test datasets. Experimental results include Unet, Unet++, DeepLabV3, DeepLabV3++, and FPN segmentation architectures. According to the comparative evaluations, mixed datasets, where public and private datasets were used together, provided the best results. The evaluations also show that the collected dataset with ground truth images provided promising results.

Keywords

Skin lesion segmentation Deep learning architectures Unet DeepLabV3 Feature pyramids network

Supporting Institution

TÜBİTAK

Project Number

122E629

Thanks

The authors thanks to TÜBİTAK for their supports

References

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  • N. C. F. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC),” Proc. - Int. Symp. Biomed. Imaging, vol. 2018-April, pp. 168–172, Oct. 2017, doi: 10.1109/ISBI.2018.8363547.
  • N. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC),” Feb. 2019, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1902.03368v2.
  • C. Hernández-Pérez et al., “BCN20000: Dermoscopic Lesions in the Wild,” Sci. Data, vol. 11, no. 1, Aug. 2019, doi: 10.1038/s41597-024-03387-w.
  • P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci. Data 2018 51, vol. 5, no. 1, pp. 1–9, Aug. 2018, doi: 10.1038/sdata.2018.161.
  • O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
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  • L. Zhang, J. Shen, and B. Zhu, “A research on an improved Unet-based concrete crack detection algorithm,” Struct. Heal. Monit., vol. 20, no. 4, pp. 1864–1879, Jul. 2021, doi: 10.1177/1475921720940068/ASSET/IMAGES/10.1177_1475921720940068-IMG1.PNG.
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  • D.-Y. Chen et al., “Building Extraction and Number Statistics in WUI Areas Based on UNet Structure and Ensemble Learning,” Remote Sens. 2021, Vol. 13, Page 1172, vol. 13, no. 6, p. 1172, Mar. 2021, doi: 10.3390/RS13061172.
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  • L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11211 LNCS, pp. 833–851, Feb. 2018, doi: 10.1007/978-3-030-01234-2_49.
  • O. Oktay et al., “Attention U-Net: Learning Where to Look for the Pancreas,” Apr. 2018, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1804.03999v3.
  • X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Med. Image Anal., vol. 58, p. 101552, Dec. 2019, doi: 10.1016/J.MEDIA.2019.101552.
  • V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 12, pp. 2481–2495, Nov. 2015, doi: 10.1109/TPAMI.2016.2644615.
  • E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Nov. 2014, doi: 10.1109/TPAMI.2016.2572683.
  • M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation,” Feb. 2018, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1802.06955v5.
  • F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” Proc. - 2016 4th Int. Conf. 3D Vision, 3DV 2016, pp. 565–571, Jun. 2016, doi: 10.1109/3DV.2016.79.
  • N. Ibtehaz and M. S. Rahman, “MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation,” Neural Networks, vol. 121, pp. 74–87, Feb. 2019, doi: 10.1016/j.neunet.2019.08.025.
  • H. Sharen, M. Jawahar, L. Jani Anbarasi, V. Ravi, N. Saleh Alghamdi, and W. Suliman, “FDUM-Net: An enhanced FPN and U-Net architecture for skin lesion segmentation,” Biomed. Signal Process. Control, vol. 91, p. 106037, May 2024, doi: 10.1016/J.BSPC.2024.106037.
  • Sweta Jain, Pruthviraj Choudhari, Mahesh Gour, Pulmonary Lung Nodule Detection from Computed Tomography Images Using Two-Stage Convolutional Neural Network, The Computer Journal, Volume 66, Issue 4, April 2023, Pages 785–795.
  • He, X., Wang, Y., Poiesi, F., Song, W., Xu, Q., Feng, Z., & Wan, Y. (2023). Exploiting multi-granularity visual features for retinal layer segmentation in human eyes. Frontiers in Bioengineering and Biotechnology, 11, 1191803.
  • Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
  • Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis, 58, 101552. https://doi.org/10.1016/j.media.2019.101552
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
  • M. K. Hasan, M. A. Ahamad, C. H. Yap, and G. Yang, “A survey, review, and future trends of skin lesion segmentation and classification,” Comput. Biol. Med., vol. 155, p. 106624, Mar. 2023, doi: 10.1016/J.COMPBIOMED.2023.106624.
  • M. Strzelecki, M. Kociołek, M. Strąkowska, M. Kozłowski, A. Grzybowski, and P. M. Szczypiński, “Artificial intelligence in the detection of skin cancer: State of the art,” Clin. Dermatol., vol. 42, no. 3, pp. 280–295, May 2024, doi: 10.1016/J.CLINDERMATOL.2023.12.022.
  • T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” Dec. 2016, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1612.03144v2.
  • R. L. Araújo, F. H. D. d. Araújo, and R. R. V. e. Silva, “Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning,” Multimed. Syst., vol. 28, no. 4, pp. 1239–1250, Aug. 2022, doi: 10.1007/S00530-021-00840-3/TABLES/8.
  • R. Mohakud and R. Dash, “Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 9889–9904, Nov. 2022, doi: 10.1016/J.JKSUCI.2021.12.018.

Year 2024, Volume: 7 Issue: 3, 449 - 459, 31.12.2024

Gökçen Çetinel , Bekir Murat Aydın , Sevda Gül , Devrim Akgün , Rabia Öztaş Kara

https://doi.org/10.35377/saucis...1543993

Abstract

Project Number

122E629

References

  • S. Spanos et al., “Measuring the quality of skin cancer management in primary care: A scoping review,” Australas. J. Dermatol., vol. 64, no. 2, pp. 177–193, May 2023, doi: 10.1111/AJD.14023.
  • R. Javed, M. S. M. Rahim, T. Saba, and A. Rehman, “A comparative study of features selection for skin lesion detection from dermoscopic images,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 9, no. 1, pp. 1–13, Dec. 2020, doi: 10.1007/S13721-019-0209-1/TABLES/5.
  • M. Zafar, M. I. Sharif, M. I. Sharif, S. Kadry, S. A. C. Bukhari, and H. T. Rauf, “Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey,” Life 2023, Vol. 13, Page 146, vol. 13, no. 1, p. 146, Jan. 2023, doi: 10.3390/LIFE13010146.
  • Z. Mirikharaji et al., “A survey on deep learning for skin lesion segmentation,” Med. Image Anal., vol. 88, p. 102863, Aug. 2023, doi: 10.1016/J.MEDIA.2023.102863.
  • N. C. F. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC),” Proc. - Int. Symp. Biomed. Imaging, vol. 2018-April, pp. 168–172, Oct. 2017, doi: 10.1109/ISBI.2018.8363547.
  • N. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC),” Feb. 2019, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1902.03368v2.
  • C. Hernández-Pérez et al., “BCN20000: Dermoscopic Lesions in the Wild,” Sci. Data, vol. 11, no. 1, Aug. 2019, doi: 10.1038/s41597-024-03387-w.
  • P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci. Data 2018 51, vol. 5, no. 1, pp. 1–9, Aug. 2018, doi: 10.1038/sdata.2018.161.
  • O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
  • N. J. Singh and K. Nongmeikapam, “Semantic Segmentation of Satellite Images Using Deep-Unet,” Arab. J. Sci. Eng., vol. 48, no. 2, pp. 1193–1205, Feb. 2023, doi: 10.1007/S13369-022-06734-4/TABLES/2.
  • L. Zhang, J. Shen, and B. Zhu, “A research on an improved Unet-based concrete crack detection algorithm,” Struct. Heal. Monit., vol. 20, no. 4, pp. 1864–1879, Jul. 2021, doi: 10.1177/1475921720940068/ASSET/IMAGES/10.1177_1475921720940068-IMG1.PNG.
  • D. Harrison, F. C. De Leo, W. J. Gallin, F. Mir, S. Marini, and S. P. Leys, “Machine Learning Applications of Convolutional Neural Networks and Unet Architecture to Predict and Classify Demosponge Behavior,” Water 2021, Vol. 13, Page 2512, vol. 13, no. 18, p. 2512, Sep. 2021, doi: 10.3390/W13182512.
  • D.-Y. Chen et al., “Building Extraction and Number Statistics in WUI Areas Based on UNet Structure and Ensemble Learning,” Remote Sens. 2021, Vol. 13, Page 1172, vol. 13, no. 6, p. 1172, Mar. 2021, doi: 10.3390/RS13061172.
  • Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11045 LNCS, pp. 3–11, 2018, doi: 10.1007/978-3-030-00889-5_1/FIGURES/3.
  • L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11211 LNCS, pp. 833–851, Feb. 2018, doi: 10.1007/978-3-030-01234-2_49.
  • O. Oktay et al., “Attention U-Net: Learning Where to Look for the Pancreas,” Apr. 2018, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1804.03999v3.
  • X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Med. Image Anal., vol. 58, p. 101552, Dec. 2019, doi: 10.1016/J.MEDIA.2019.101552.
  • V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 12, pp. 2481–2495, Nov. 2015, doi: 10.1109/TPAMI.2016.2644615.
  • E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Nov. 2014, doi: 10.1109/TPAMI.2016.2572683.
  • M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation,” Feb. 2018, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1802.06955v5.
  • F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” Proc. - 2016 4th Int. Conf. 3D Vision, 3DV 2016, pp. 565–571, Jun. 2016, doi: 10.1109/3DV.2016.79.
  • N. Ibtehaz and M. S. Rahman, “MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation,” Neural Networks, vol. 121, pp. 74–87, Feb. 2019, doi: 10.1016/j.neunet.2019.08.025.
  • H. Sharen, M. Jawahar, L. Jani Anbarasi, V. Ravi, N. Saleh Alghamdi, and W. Suliman, “FDUM-Net: An enhanced FPN and U-Net architecture for skin lesion segmentation,” Biomed. Signal Process. Control, vol. 91, p. 106037, May 2024, doi: 10.1016/J.BSPC.2024.106037.
  • Sweta Jain, Pruthviraj Choudhari, Mahesh Gour, Pulmonary Lung Nodule Detection from Computed Tomography Images Using Two-Stage Convolutional Neural Network, The Computer Journal, Volume 66, Issue 4, April 2023, Pages 785–795.
  • He, X., Wang, Y., Poiesi, F., Song, W., Xu, Q., Feng, Z., & Wan, Y. (2023). Exploiting multi-granularity visual features for retinal layer segmentation in human eyes. Frontiers in Bioengineering and Biotechnology, 11, 1191803.
  • Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
  • Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis, 58, 101552. https://doi.org/10.1016/j.media.2019.101552
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
  • M. K. Hasan, M. A. Ahamad, C. H. Yap, and G. Yang, “A survey, review, and future trends of skin lesion segmentation and classification,” Comput. Biol. Med., vol. 155, p. 106624, Mar. 2023, doi: 10.1016/J.COMPBIOMED.2023.106624.
  • M. Strzelecki, M. Kociołek, M. Strąkowska, M. Kozłowski, A. Grzybowski, and P. M. Szczypiński, “Artificial intelligence in the detection of skin cancer: State of the art,” Clin. Dermatol., vol. 42, no. 3, pp. 280–295, May 2024, doi: 10.1016/J.CLINDERMATOL.2023.12.022.
  • T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” Dec. 2016, Accessed: Jul. 12, 2024. [Online]. Available: https://arxiv.org/abs/1612.03144v2.
  • R. L. Araújo, F. H. D. d. Araújo, and R. R. V. e. Silva, “Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning,” Multimed. Syst., vol. 28, no. 4, pp. 1239–1250, Aug. 2022, doi: 10.1007/S00530-021-00840-3/TABLES/8.
  • R. Mohakud and R. Dash, “Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 9889–9904, Nov. 2022, doi: 10.1016/J.JKSUCI.2021.12.018.
There are 33 citations in total.

Details

Primary Language English
Subjects Software Testing, Verification and Validation
Journal Section Research Article
Authors

Gökçen Çetinel 0000-0002-1999-2797

Bekir Murat Aydın 0000-0002-5965-0687

Sevda Gül 0000-0002-7040-7952

Devrim Akgün 0000-0002-0770-599X

Rabia Öztaş Kara 0000-0003-1828-5844

Project Number 122E629
Early Pub Date December 25, 2024
Publication Date December 31, 2024
Submission Date September 5, 2024
Acceptance Date November 20, 2024
Published in Issue Year 2024Volume: 7 Issue: 3

Cite

IEEE G. Çetinel, B. M. Aydın, S. Gül, D. Akgün, and R. Öztaş Kara, “An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures”, SAUCIS, vol. 7, no. 3, pp. 449–459, 2024, doi: 10.35377/saucis...1543993.
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