Publication:
Clinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma.

dc.contributor.authorSerrano, Carmen
dc.contributor.authorLazo, Manuel
dc.contributor.authorSerrano, Amalia
dc.contributor.authorToledo-Pastrana, Tomás
dc.contributor.authorBarros-Tornay, Rubén
dc.contributor.authorAcha, Begoña
dc.date.accessioned2023-05-03T14:11:09Z
dc.date.available2023-05-03T14:11:09Z
dc.date.issued2022-07-12
dc.description.abstractBackground and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.
dc.identifier.doi10.3390/jimaging8070197
dc.identifier.essn2313-433X
dc.identifier.pmcPMC9319034
dc.identifier.pmid35877641
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319034/pdf
dc.identifier.unpaywallURLhttps://www.mdpi.com/2313-433X/8/7/197/pdf?version=1658389678
dc.identifier.urihttp://hdl.handle.net/10668/21366
dc.issue.number7
dc.journal.titleJournal of imaging
dc.journal.titleabbreviationJ Imaging
dc.language.isoen
dc.organizationHospital Universitario Virgen Macarena
dc.pubmedtypeJournal Article
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectbasal cell carcinoma
dc.subjectclinically inspired classification
dc.subjectcolor appearance models
dc.subjectcolor cooccurrence matrix
dc.subjectdeep learning
dc.subjectdermatology
dc.titleClinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number8
dspace.entity.typePublication

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