عنوان انگلیسی مقاله:
Transform domain representation-driven convolutional neural networks for skin lesion segmentation
ترجمه فارسی عنوان مقاله:
انتقال شبکه های عصبی کانولوشن نمایندگی محور دامنه برای تقسیم بندی ضایعه پوستی
Sciencedirect - Elsevier - Expert Systems With Applications, 144 (2020) 113129: doi:10:1016/j:eswa:2019:113129
Mansoureh Pezhman Pour a , ∗, Huseyin Seker b
Automated diagnosis systems provide a huge improvement in early detection of skin cancer, and con- sequently, contribute to successful treatment. Recent research on convolutional neural network has achieved enormous success in segmentation and object detection tasks. However, these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found. Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of im- ages in terms of light, color, scale, and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images. A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation. The network is trained from scratch and despite the small size of datasets neither excessive data augmentation nor any preprocessing to remove artifacts or enhance the images are applied. Alternatively, we investigated incorporating image represen- tations of the transform domain to the convolutional neural network and compared to a model with more convolutional layers that resulted in 6% higher Jaccard index and has shorter training time. The model improved by applying CIELAB color space and the performance of the final proposed architecture is evaluated on publicly available datasets from ISBI challenges in 2016 and 2017. The proposed model has resulted in an improvement of as much as 7% for the segmentation metrics and 17% for the fea- ture segmentation, which demonstrates the robustness of this unique hybrid framework and its future applications as well as further improvement.
Keywords: Convolutional neural network | Dermoscopic features | Melanoma | Skin lesion segmentation | Transform domain