DSNet: Automatic Dermoscopic Skin Lesion Segmentation

Autor: Prasad N. Samarakoon, Lavsen Dahal, Fakrul Islam Tushar, Md. Kamrul Hasan, Robert Marti Marly
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
0301 basic medicine
Skin Neoplasms
Source code
Computer science
Computer Vision and Pattern Recognition (cs.CV)
media_common.quotation_subject
Computer Science - Computer Vision and Pattern Recognition
Dermoscopy
Health Informatics
CAD
Skin Diseases
Convolution
03 medical and health sciences
0302 clinical medicine
FOS: Electrical engineering
electronic engineering
information engineering
Humans
Segmentation
Melanoma
Skin
media_common
Pixel
Intersection (set theory)
business.industry
Deep learning
Image and Video Processing (eess.IV)
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Computer Science Applications
030104 developmental biology
Neural Networks
Computer
Artificial intelligence
business
Encoder
030217 neurology & neurosurgery
Popis: Automatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation remains a challenging task due to their diverse color, texture, and indistinguishable boundaries and forms an open problem. Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used depth-wise separable convolution in lieu of standard convolution to project the learned discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed DSNet. We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is 77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and 6.8 % with respect to mIoU on the ISIC-2017 dataset. Our network for skin lesion segmentation outperforms other methods and can provide better segmented masks on two different test datasets which can lead to better performance in melanoma detection. Our trained model along with the source code and predicted masks are made publicly available.
25 pages
Databáze: OpenAIRE
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