Melanoma Skin Cancer Detection based on Image Processing.
Identifieur interne : 000647 ( Main/Exploration ); précédent : 000646; suivant : 000648Melanoma Skin Cancer Detection based on Image Processing.
Auteurs : Nadia Smaoui Zghal [Tunisie] ; Nabil Derbel [Tunisie]Source :
- Current medical imaging reviews ; 2020.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Dermoscopie (MeSH), Diagnostic assisté par ordinateur (MeSH), Humains (MeSH), Mélanome (anatomopathologie), Mélanome (imagerie diagnostique), Reproductibilité des résultats (MeSH), Traitement d'image par ordinateur (méthodes), Tumeurs cutanées (anatomopathologie), Tumeurs cutanées (imagerie diagnostique).
- MESH :
- anatomopathologie : Mélanome, Tumeurs cutanées.
- imagerie diagnostique : Mélanome, Tumeurs cutanées.
- méthodes : Traitement d'image par ordinateur.
- Algorithmes, Dermoscopie, Diagnostic assisté par ordinateur, Humains, Reproductibilité des résultats.
English descriptors
- KwdEn :
- MESH :
- diagnostic imaging : Melanoma, Skin Neoplasms.
- methods : Image Processing, Computer-Assisted.
- pathology : Melanoma, Skin Neoplasms.
- Algorithms, Dermoscopy, Diagnosis, Computer-Assisted, Humans, Reproducibility of Results.
Abstract
BACKGROUND
Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient's survival likelihood.
AIMS
This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules.
METHODS
The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer.
RESULTS AND CONCLUSION
Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.
DOI: 10.2174/1573405614666180911120546
PubMed: 31989893
Affiliations:
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Engineers of Sfax, University of Sfax, Sfax, Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Industrial Computer, Control and Energy Management Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax</wicri:regionArea><wicri:noRegion>Sfax</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:31989893</idno><idno type="pmid">31989893</idno><idno type="doi">10.2174/1573405614666180911120546</idno><idno type="wicri:Area/PubMed/Corpus">000172</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000172</idno><idno type="wicri:Area/PubMed/Curation">000171</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000171</idno><idno type="wicri:Area/PubMed/Checkpoint">000117</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" 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uniqKey="Derbel N" first="Nabil" last="Derbel">Nabil Derbel</name><affiliation wicri:level="1"><nlm:affiliation>Industrial Computer, Control and Energy Management Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax, Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Industrial Computer, Control and Energy Management Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax</wicri:regionArea><wicri:noRegion>Sfax</wicri:noRegion></affiliation></author></analytic><series><title level="j">Current medical imaging reviews</title><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Dermoscopy (MeSH)</term><term>Diagnosis, Computer-Assisted (MeSH)</term><term>Humans (MeSH)</term><term>Image Processing, Computer-Assisted (methods)</term><term>Melanoma (diagnostic imaging)</term><term>Melanoma (pathology)</term><term>Reproducibility of Results (MeSH)</term><term>Skin Neoplasms (diagnostic imaging)</term><term>Skin Neoplasms (pathology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Dermoscopie (MeSH)</term><term>Diagnostic assisté par ordinateur (MeSH)</term><term>Humains (MeSH)</term><term>Mélanome (anatomopathologie)</term><term>Mélanome (imagerie diagnostique)</term><term>Reproductibilité des résultats (MeSH)</term><term>Traitement d'image par ordinateur (méthodes)</term><term>Tumeurs cutanées (anatomopathologie)</term><term>Tumeurs cutanées (imagerie diagnostique)</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Mélanome</term><term>Tumeurs cutanées</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Melanoma</term><term>Skin Neoplasms</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Mélanome</term><term>Tumeurs cutanées</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Traitement d'image par ordinateur</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Melanoma</term><term>Skin Neoplasms</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Dermoscopy</term><term>Diagnosis, Computer-Assisted</term><term>Humans</term><term>Reproducibility of Results</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Dermoscopie</term><term>Diagnostic assisté par ordinateur</term><term>Humains</term><term>Reproductibilité des résultats</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient's survival likelihood.</p></div><div type="abstract" xml:lang="en"><p><b>AIMS</b></p><p>This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS AND CONCLUSION</b></p><p>Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.</p></div></front></TEI><affiliations><list><country><li>Tunisie</li></country></list><tree><country name="Tunisie"><noRegion><name sortKey="Zghal, Nadia Smaoui" sort="Zghal, Nadia Smaoui" uniqKey="Zghal N" first="Nadia Smaoui" last="Zghal">Nadia Smaoui Zghal</name></noRegion><name sortKey="Derbel, Nabil" sort="Derbel, Nabil" uniqKey="Derbel N" first="Nabil" last="Derbel">Nabil Derbel</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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