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LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer

Author

Listed:
  • Cihan Akyel

    (Graduate School of Informatics, Management Information Systems, Gazi University, Ankara 06560, Turkey)

  • Nursal Arıcı

    (Management Information Systems Department, Applied Sciences Faculty, Gazi University, Ankara 06560, Turkey)

Abstract

Skin cancer is common nowadays. Early diagnosis of skin cancer is essential to increase patients’ survival rate. In addition to traditional methods, computer-aided diagnosis is used in diagnosis of skin cancer. One of the benefits of this method is that it eliminates human error in cancer diagnosis. Skin images may contain noise such as like hair, ink spots, rulers, etc., in addition to the lesion. For this reason, noise removal is required. The noise reduction in lesion images can be referred to as noise removal. This phase is very important for the correct segmentation of the lesions. One of the most critical problems in using such automated methods is the inaccuracy in cancer diagnosis because noise removal and segmentation cannot be performed effectively. We have created a noise dataset (hair, rulers, ink spots, etc.) that includes 2500 images and masks. There is no such noise dataset in the literature. We used this dataset for noise removal in skin cancer images. Two datasets from the International Skin Imaging Collaboration (ISIC) and the PH2 were used in this study. In this study, a new approach called LinkNet-B7 for noise removal and segmentation of skin cancer images is presented. LinkNet-B7 is a LinkNet-based approach that uses EfficientNetB7 as the encoder. We used images with 16 slices. This way, we lose fewer pixel values. LinkNet-B7 has a 6% higher success rate than LinkNet with the same dataset and parameters. Training accuracy for noise removal and lesion segmentation was calculated to be 95.72% and 97.80%, respectively.

Suggested Citation

  • Cihan Akyel & Nursal Arıcı, 2022. "LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:736-:d:758954
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    Citations

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    Cited by:

    1. V. Auxilia Osvin Nancy & P. Prabhavathy & Meenakshi S. Arya, 2024. "Role of Artificial Intelligence and Deep Learning in Skin Disease Prediction: A Systematic Review and Meta-analysis," Annals of Data Science, Springer, vol. 11(6), pages 2109-2139, December.
    2. Varun Srivastava & Shilpa Gupta & Ritik Singh & VaibhavKumar Gautam, 2024. "A multi-level closing based segmentation framework for dermatoscopic images using ensemble deep network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3926-3939, August.

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