DIAGNOSING SKIN MELANOMA: CURRENT VERSUS FUTURE DIRECTIONS

TASK Quarterly

JERZY W. GRZYMAŁA-BUSSE¹  JAN P. GRZYMAŁA-BUSSE¹  MARIUSZ WRZESIEŃ²  PIOTR BLAJDO²  MAKSYMILIAN KNAP²  ZDZISŁAW S. HIPPE²  WIESŁAW PAJA²  STANISŁAW BAJCAR³ 

[1] Department of Electrical Engineering and Computer Science, University of Kansas Lawrence, KS 66045, USA;Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszow, Poland;Regional Dermatology Center, Warzywna 3, 35-310 Rzeszow, Poland;

关键词: melanoma;    TDS;    machine learning in diagnosis of melanoma;   

DOI  :  

来源: DOAJ

【 摘 要 】

A new database containing 410 cases of nevi pigmentosi, in four categories: benign nevus, blue nevus, suspicious nevus and melanoma malignant, carefully verified by histopathology, is described. The database is entirely different from the base presented previously, and can be readily used for research based on the socalled constructive induction in machine learning. To achieve this, the database features a different set of thirteen descriptive attributes, with a fourteenth additional attribute computed by applying values of the remaining thirteen attributes. In addition, a new program environment for the validation of computer-assisted diagnosis of melanoma, is briefly discussed. Finally, results are presented on determining optimal coefficients for the well-known ABCD formula, useful for melanoma diagnosis.

【 授权许可】

Unknown