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


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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