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AuthorAnjum, Muhammad Almas
AuthorAmin, Javaria
AuthorSharif, Muhammad
AuthorKhan, Habib Ullah
AuthorMalik, Muhammad Sheraz Arshad
AuthorKadry, Seifedine
Available date2022-12-29T06:03:43Z
Publication Date2020-07-14
Publication NameIEEE Access
Identifierhttp://dx.doi.org/10.1109/ACCESS.2020.3009276
CitationAnjum, M. A., Amin, J., Sharif, M., Khan, H. U., Malik, M. S. A., & Kadry, S. (2020). Deep semantic segmentation and multi-class skin lesion classification based on convolutional neural network. IEEE Access, 8, 129668-129678.
ISSN2169-3536
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089508765&origin=inward
URIhttp://hdl.handle.net/10576/37772
AbstractSkin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.
SponsorQatar University [IRCC-2020-009].
Languageen
PublisherIEEE
Subjectant colony optimization
ONNX
ResNet-18
squeeze Net
SVM
YOLOv2
TitleDeep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network
TypeArticle
Pagination129668-129678
Volume Number8
dc.accessType Open Access


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