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Related Experiment Video

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A 3D Organotypic Melanoma Spheroid Skin Model

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer.

Panagiota Spyridonos¹, George Gaitanis², Aristidis Likas³

¹Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece.

|December 24, 2021

Summary

This summary is machine-generated.

This study developed a computer-based method using dermoscopy images to help differentiate seborrheic keratosis-like malignant melanomas (SK-like MMs). The approach achieved high accuracy, aiding in the diagnosis of these challenging skin lesions.

Keywords:

SK-like MM deep learning knowledge transfer melanoma seborrheic keratosis

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Area of Science:

Dermatology and Computer Science
Medical Image Analysis
Artificial Intelligence in Healthcare

Background:

Seborrheic keratosis-like malignant melanomas (SK-like MMs) pose diagnostic challenges, risking delayed treatment.
Distinguishing SK-like MMs from benign seborrheic keratosis (SK) is crucial to avoid unnecessary excisions or missed diagnoses.

Purpose of the Study:

To develop and validate a computer-based diagnostic approach for characterizing SK-like MMs using dermoscopy images.
To improve the accuracy and efficiency of diagnosing atypical melanomas that mimic benign skin lesions.

Main Methods:

Utilized dermoscopy images from the International Skin Imaging Collaboration archive.
Employed a convolutional neural network (VGG16) for image embeddings and trained a support vector machine (SVM) classifier.
Optimized SVM hyperparameters using Bayesian optimization and tested on an independent dataset.

Main Results:

The SVM classifier demonstrated a sensitivity of 78.6% and a specificity of 84.5% in identifying atypical MMs.
Key differentiating features included the presence/absence of pigmented networks and milia-like cysts.

Conclusions:

Computer-based analysis of dermoscopy images shows promise for characterizing challenging SK-like MMs.
Deep learning and machine learning models can significantly aid in complex dermatological diagnostic problems.