Melanoma and nevus classification based on asymmetry, border, color, and GLCM texture parameters using deep learning algorithm

Theodore Gautama Chandra*, Aulia M.T. Nasution, Iwan Cony Setiadi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Pattern analysis has been shown to have higher reliability for melanoma and nevus classification. The ABCD method, which is common to be used as a melanoma diagnosis method, has been shown to have inappropriate weighting for each parameter. In addition, pattern analysis has been shown to have a higher success for diagnosing melanoma. In this paper, we choose the Grey Level Co-occurrence Matrix (GLCM) as a texture parameter to represent the pattern of melanoma. We also choose Deep Neural Network (DNN) to retrieve information from the data set. DNN has the capability of analyzing data with a high level of abstraction. Therefore, we choose DNN as a method to classify melanoma and nevus. We use the International Skin Imaging Collaboration (ISIC) archive database as our training and validation data. We use 773 nevi with 870 melanoma images as training data and separate 200 Nevus and 200 Melanoma images as validation data. We achieve 81.75% diagnostic accuracy, 75.5% sensitivity, and 88% specificity.

Original languageEnglish
Title of host publication4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices
Subtitle of host publicationProceedings of the International Symposium of Biomedical Engineering, ISBE 2019
EditorsKenny Lischer, Tomy Abuzairi, Siti Fauziyah Rahman, Misri Gozan
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419445
DOIs
Publication statusPublished - 10 Dec 2019
Event4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019 - Padang, West Sumatera, Indonesia
Duration: 22 Jul 201924 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2193
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019
Country/TerritoryIndonesia
CityPadang, West Sumatera
Period22/07/1924/07/19

Keywords

  • Asymmetry
  • Border
  • Classification
  • Color
  • Deep Neural Network
  • GLCM Texture
  • Melanoma
  • Nevus

Fingerprint

Dive into the research topics of 'Melanoma and nevus classification based on asymmetry, border, color, and GLCM texture parameters using deep learning algorithm'. Together they form a unique fingerprint.

Cite this