Literature DB >> 17271970

Pattern classification of nevus with texture analysis.

T Tanaka1, S Torii, I Kabuta, K Shimizu, M Tanaka, H Oka.   

Abstract

The purpose of this research is to classify the pattern on the surface of the nevus. The digital image that contains one nevus is classified into three kinds of patterns of homogeneous pattern, globular pattern, and reticular pattern by the texture analysis. The tumor part in the image is specified first, and the specified tumor part is divided into some sub-images. Afterwards, the amount of the texture features of each sub-image was calculated. The pattern was classified by the discriminant analysis based on the amount of the texture features. As a result, the patterns could be classified correctly into three categories at the ratio of 94%.

Entities:  

Year:  2004        PMID: 17271970     DOI: 10.1109/IEMBS.2004.1403450

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

Authors:  J Premaladha; K S Ravichandran
Journal:  J Med Syst       Date:  2016-02-12       Impact factor: 4.460

Review 2.  Cancer Diagnosis Using Deep Learning: A Bibliographic Review.

Authors:  Khushboo Munir; Hassan Elahi; Afsheen Ayub; Fabrizio Frezza; Antonello Rizzi
Journal:  Cancers (Basel)       Date:  2019-08-23       Impact factor: 6.639

Review 3.  Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms.

Authors:  Ammara Masood; Adel Ali Al-Jumaily
Journal:  Int J Biomed Imaging       Date:  2013-12-23
  3 in total

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