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PROJECT TITLE:

Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images

ABSTRACT:

Thickness of the melanoma is the most necessary issue related to survival in patients with melanoma. It is most typically reported as a measurement of depth given in millimeters (mm) and computed by suggests that of pathological examination after a biopsy of the suspected lesion. In order to avoid the utilization of an invasive methodology within the estimation of the thickness of melanoma before surgery, we tend to propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. 2 supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a 3-category theme (skinny, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the 3-class downside, a collection of ordinal classification strategies (considering ordering relation between the 3 classes) is included. For the binary case, LIPU outperforms all the opposite ways with an accuracy of 77.six%, whereas, for the second theme, though LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all categories.


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