Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Use of a Mobile Phone as a Vibrating Device to Ameliorate Pain During Injections.

Indian dermatology online journal·2025

Same author

Reply to comment to "Chemical peel recipe"-Caution. Phenol is not safe for use for chemo-exfoliation.

Journal of cosmetic dermatology·2023

Same author

Cross-sectional study to evaluate the utility of elastic tissue staining in primary cicatricial alopecia.

Journal of clinical pathology·2023

Same author

"Chemical Peel Recipe"-An easy and quick method of peel preparation in resource-limited settings.

Journal of cosmetic dermatology·2023

Same author

Dermoscopy guided thermolysis-A modification to improve ease of procedure.

Journal of cosmetic dermatology·2022

Same author

Optimizing full face subcision by means of a three-way connector and local anesthetic-filled syringe.

Journal of cosmetic dermatology·2022

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

Pranav Parekh¹, Richard Oyeleke¹, Tejas Vishwanath²

¹Stevens Institute of Technology, Hoboken, NJ, United States.

JMIR Dermatology

|December 18, 2024

Summary

This study introduces a novel method for estimating skin lesion depth using AI and 3D holograms, improving melanoma diagnosis. The technique accurately differentiates benign and malignant lesions, offering a noninvasive tool for dermatologists.

Keywords:

ML MR XAI artificial intelligence computer graphics computer vision explainable AI machine learning mixed reality neural networks red spot analysis visualization

More Related Videos

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Related Experiment Videos

Last Updated: Jun 4, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Dermatology

Background:

Accurate skin lesion depth estimation is crucial for clinical staging, yet current methods are often approximate.
Noninvasive techniques for depth estimation from 2D images are needed, especially for rapidly growing malignant lesions.

Purpose of the Study:

To propose a novel methodology for skin lesion depth estimation and visualization using AI and 3D holograms.
To generate 3D visualizations aiding dermatologists in diagnosing melanoma and other skin conditions.
To provide a definite estimate and visualization procedure for skin lesions beyond current approximate methods.

Main Methods:

Convolutional Neural Network (CNN) for lesion classification, coupled with explainable AI (gradient class activation mapping) for feature localization.
Computer graphics and depth from defocus methods for 3D structure and depth estimation from single images.
Red spot analysis for infection degree measurement and Gabor filters for volumetric depth map representation, culminating in conical hologram generation.

Main Results:

The combined CNN and explainable AI model achieved 86% accuracy in classifying lesions as benign or malignant.
Benign and malignant cases were successfully mapped to distinct conical representations, with malignant lesions showing deeper structures.
Dermatologists provided positive feedback, recognizing the potential clinical utility of the 3D hologram visualization for lesion depth estimation.

Conclusions:

Malignant lesions exhibit a higher concentration of red spots and deeper conical sections compared to benign lesions, correlating with CNN classification.
The qualitative assessment of the 3D holograms aligns with the initial AI-based classification, validating the method's accuracy.
The positive reception from dermatologists indicates the method's potential as a valuable clinical tool for noninvasive lesion depth assessment.