Semantic Segmentation of Lesions from Dermoscopic Images using Yolo-DeepLab Networks
- سال انتشار: 1400
- محل انتشار: ماهنامه بین المللی مهندسی، دوره: 34، شماره: 2
- کد COI اختصاصی: JR_IJE-34-2_018
- زبان مقاله: انگلیسی
- تعداد مشاهده: 356
نویسندگان
Department of Industrial Engineering, K. N. Toosi University of Technology, Pardis Street, Molla Sadra Ave, Tehran, Iran
Department of Industrial Engineering, K. N. Toosi University of Technology, Pardis Street, Molla Sadra Ave, Tehran, Iran
Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran
چکیده
Accurate segmentation of lesions from dermoscopic images is very important for timely diagnosis and treatment of skin cancers. Due to the variety of shapes, sizes, colors, and locations of lesions in dermoscopic images, automatic segmentation of skin lesions remains a challenge. In this study, a two-stage method for the segmentation of skin lesions based on deep learning is presented. In the first stage, convolutional neural networks (CNNs) estimate the approximate size and location of the lesion. A sub-image around the estimated bounding box is cropped from the original image. The sub-image is resized to an image of a predefined size. In order to segment the exact area of the lesion from the normal image, other CNNs are used in the DeepLab structure. The accuracy of the normalization stage has a significant impact on the final performance. In order to increase the normalization accuracy, a combination of four networks in the structure of Yolov۳ is used. Two approaches are proposed to combine Yolov۳ structures. The segmentation results of two networks in the DeepLab v۳+ structure are also combined to improve the performance of the second stage. Another challenge is the small number of training images. To overcome this problem, the data augmentation is used, as well as using different modes of an image in each stage. In order to evaluate the proposed method, experiments are performed on the well-known ISBI ۲۰۱۷ dataset. Experimental results show that the proposed lesion segmentation method outperforms the state-of-the-art methods.کلیدواژه ها
Semantic segmentation, Skin lesion, Deep Learning, Yolov۳, DeepLab۳+اطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.