Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma
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Computerized prescreening of suspicious moles and lesions for malignancy is of great importance for assessing the need and the priority of the removal surgery. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation is accurate detection of lesion’s region, i.e., segmentation of an image into two regions as lesion and normal skin.
In this paper, a new method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed, and then, its patches are fed to a convolutional neural network. Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is proposed for more accurate detection of a lesion’s border.
Our results indicate that the proposed method could reach the accuracy of 98.7% and the sensitivity of 95.2% in segmentation of lesion regions over the dataset of clinical images.
The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
KeywordsConvolutional neural network Deep learning Medical image segmentation Melanoma excision Skin cancer
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
This articles does not contain patient data.
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