Accepted for/Published in: JMIR Dermatology
Date Submitted: Apr 23, 2024
Date Accepted: Nov 21, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology
ABSTRACT
Background:
Thus far, considerable research has been focused on classifying a lesion as benign or malignant.
Objective:
To propose a novel methodology for the depth estimation and visualization of skin lesions.
Methods:
We start by doing the same using a CNN, followed by using Explainable AI (XAI) to localize the image features responsible for the CNN output. We apply computer graphics for depth estimation and developing the 3D structure of the lesion. Our novel method, called the red spot analysis, measures the degree of infection based on which a conical hologram is constructed. Physicians can study this hologram via Mixed Reality headsets.
Results:
The neural model achieves an accuracy of 85.61%. We successfully obtained 3D representations of lesion depth using the method stated above.
Conclusions:
When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin. Clinical Trial: We do not perform RCT for this study.
Citation
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