Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of melanocytic lesions consequent of unfettered proliferative growth as a malignant lesion. This study explores the applications of hierarchical density-based spatial clustering of applications with noise (HDBSCAN) in terms of the direct diagnostic implications of applying agglomerative clustering in the spectroscopic analysis of malignant melanocytic lesions and benign dermatologic spots. 100 images of benign (n = 50) and malignant moles (n = 50) were sampled from the International Skin Imaging Collaboration Archive and processed through two separate Python algorithms. The first of which deconvolutes the three-digit tupled integer identifiers of pixel color in image composition into three separate matrices corresponding to the red, green and blue color channel. Statistical characterization of integer variance was utilized to determine the optimal channel for comparative analysis between malignant and benign image groups. The second applies HDBSCAN to the matrices, identifying agglomerative clustering in the dataset. The results indicate the potential diagnostic applications of HDBSCAN analysis in fast-processing dermoscopy, as optimization of cluster ing parameters according to a binary search strategy produced an accuracy of 85% in the classification of malignant and benign melanocytic lesions. Received 21 May 2021 Accepted 16 July 2021 Correspondence to Jason Yuan Ye, E-mail: jason.ye9@gmail.com This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
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