scholarly journals 359 Boundary-aware convolutional neural network for skin lesion segmentation in clinical images

2021 ◽  
Vol 141 (5) ◽  
pp. S63
Author(s):  
Y. Wang ◽  
Z. Jin ◽  
S. Zhao ◽  
X. Chen
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Segmentation ◽  
Clinical Images

scholarly journals Skin Lesion Segmentation with Improved Convolutional Neural Network

2020 ◽  
Vol 33 (4) ◽  
pp. 958-970 ◽  
Author(s):  
Şaban Öztürk ◽  
Umut Özkaya
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Segmentation

scholarly journals Skin Lesion Segmentation Based on Multi-Scale Attention Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 122811-122825
Author(s):  
Yun Jiang ◽  
Simin Cao ◽  
Shengxin Tao ◽  
Hai Zhang
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Segmentation ◽  
Multi Scale

Automated skin lesion segmentation using attention-based deep convolutional neural network

2021 ◽  
Vol 65 ◽  
pp. 102358
Author(s):  
Ridhi Arora ◽  
Balasubramanian Raman ◽  
Kritagya Nayyar ◽  
Ruchi Awasthi
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Lesion Segmentation

scholarly journals DermoNet: densely linked convolutional neural network for efficient skin lesion segmentation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Saleh Baghersalimi ◽  
Behzad Bozorgtabar ◽  
Philippe Schmid-Saugeon ◽  
Hazım Kemal Ekenel ◽  
Jean-Philippe Thiran
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Segmentation

Skin lesion segmentation using high-resolution convolutional neural network

2020 ◽  
Vol 186 ◽  
pp. 105241 ◽  
Author(s):  
Fengying Xie ◽  
Jiawen Yang ◽  
Jie Liu ◽  
Zhiguo Jiang ◽  
Yushan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Segmentation

scholarly journals Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

2020 ◽  
Vol 39 (3) ◽  
pp. 169-185
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Skin Lesions ◽  
Training Dataset ◽  
Great Success ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Low Contrast

Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

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