“Basal cell carcinoma (BCC) is one of the most common types of skin cancer in the United States. Early detection of BCC by noninvasive techniques can decrease delay in treatment and save cost. A recent study estimated that 5.4 million cases of non-melanocytic skin cancer (NMSC) occur each year in the US. BCC accounts for 50% of NMSC cases. Telangiectasia, which appears in most BCCs is an important feature for identification of BCC for an automatic diagnostic system. In this thesis, three methods for detection of telangiectasia present in dermoscopy lesion image (DI) were proposed. Detected telangiectasia in DI was used to predict BCC. Using stepwise logistic regression, a model was created for which the area under a receiver operating characteristic (ROC) curve of 88.9% was achieved for detection of BCC”--Abstract, page iii.
Moss, Randy Hays, 1953-
Stanley, R. Joe
Electrical and Computer Engineering
M.S. in Electrical Engineering
Missouri University of Science and Technology
xiii, 49 pages
© 2017 Hemanth Yadav Aradhyula, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Aradhyula, Hemanth Yadav, "Classification of basal cell carcinoma using telangiectatic vessels and machine learning" (2017). Masters Theses. 8034.