Volume 8, Issue 2 (7-2020)
Jorjani Biomed J 2020, 8(2): 58-72 |
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Bagheri F, Tarokh M J, Ziaratban M. Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks. Jorjani Biomed J 2020; 8 (2) :58-72
URL: http://goums.ac.ir/jorjanijournal/article-1-737-en.html
URL: http://goums.ac.ir/jorjanijournal/article-1-737-en.html
1- Department of Industrial Engineering, K. N. Toosi University of Technology, Pardis Street, Molla Sadra Ave, Tehran, Iran
2- Department of Industrial Engineering, K. N. Toosi University of Technology, Pardis Street, Molla Sadra Ave, Tehran, Iran ,f.bagheri@email.kntu.ac.ir
3- Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran
2- Department of Industrial Engineering, K. N. Toosi University of Technology, Pardis Street, Molla Sadra Ave, Tehran, Iran ,
3- Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran
Abstract: (8443 Views)
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.
Material and 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-the-art 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 well-known 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.
Material and 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-the-art 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 well-known 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.
Type of Article: Original article |
Subject:
Bio-statistics
Received: 2020/01/1 | Accepted: 2020/05/31 | Published: 2020/07/1
Received: 2020/01/1 | Accepted: 2020/05/31 | Published: 2020/07/1
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