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Information Journal Paper

Title

Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks

Pages

  58-72

Abstract

 Background and objective: Automatic Semantic segmentation of Skin lesions is one of the most important medical requirements in the diagnosis and treatment of skin cancer, and scientists always try to achieve more accurate lesion segmentation systems. Developing an accurate model for lesion segmentation helps in timely diagnosis and appropriate treatment. Methods: In this study, a two-stage deep learning-based method is presented for accurate segmentation of Skin lesions. At the first stage, detection stage, an approximate location of the lesion in a dermoscopy is estimated using deep Yolo v2 network. A sub-image is cropped from the input dermoscopy by considering a margin around the estimated lesion bounding box and then resized to a predetermined normal size. DeepLab convolutional neural network is used at the second stage, segmentation stage, to extract the exact lesion area from the normalized image. Results: A standard and well-known dataset of Dermoscopic images, (ISBI) 2017 dataset, is used to evaluate the proposed method and compare it with the state-of-theart methods. Our method achieved Jaccard value of 79. 05%, which is 2. 55% higher than the Jaccard of the winner of the ISIC 2017 challenge. Conclusion: Experiments demonstrated that the proposed two-stage CNN-based lesion segmentation method outperformed other state-of-the-art methods on the wellknown ISIB2017 dataset. High accuracy in detection stage is of most important. Using the detection stage based on Yolov2 before segmentation stage, DeepLab3+ structure with appropriate backbone network, data augmentation, and additional modes of input images are the main reasons of the significant improvement.

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    Cite

    APA: Copy

    BAGHERI, FATEMEH, TAROKH, MOHAMMAD JAFAR, & ZIARATBAN, MAJID. (2020). Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks. JORJANI BIOMEDICINE JOURNAL, 8(2), 58-72. SID. https://sid.ir/paper/760635/en

    Vancouver: Copy

    BAGHERI FATEMEH, TAROKH MOHAMMAD JAFAR, ZIARATBAN MAJID. Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks. JORJANI BIOMEDICINE JOURNAL[Internet]. 2020;8(2):58-72. Available from: https://sid.ir/paper/760635/en

    IEEE: Copy

    FATEMEH BAGHERI, MOHAMMAD JAFAR TAROKH, and MAJID ZIARATBAN, “Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks,” JORJANI BIOMEDICINE JOURNAL, vol. 8, no. 2, pp. 58–72, 2020, [Online]. Available: https://sid.ir/paper/760635/en

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