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Development, Application and Utility of a Machine Learning Approach for Melanoma and Non-Melanoma Lesion

Pablo Romero-Morelos^1,2, Elizabeth Herrera-López^1,2, Beatriz González-Yebra³

¹Department of Research, State University of the Valley of Ecatepec, Ecatepec 55210, México State, Mexico.

Diagnostics (Basel, Switzerland)

|June 19, 2024

Summary

Fractal dimension analysis combined with artificial intelligence offers a promising, non-invasive method for accurately classifying skin lesions, including melanoma, improving early detection rates.

Keywords:

artificial intelligence dermatological lesion fractal dimension machine learning melanoma

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Area of Science:

Dermatology and Computational Pathology
Medical Informatics and Artificial Intelligence
Mathematical Modeling in Medicine

Background:

Melanoma diagnosis can be subjective and invasive, with histopathology as the current gold standard.
There is a need for non-invasive, accurate, and efficient methods for early skin lesion detection.
Fractality, a measure of complexity, has potential in dermatological diagnostics but hasn't been used with AI.

Purpose of the Study:

To evaluate the utility of fractal dimension (FD) in classifying melanoma and non-melanoma skin lesions.
To develop and assess unsupervised machine learning models using FD for automated dermatological lesion classification.
To explore the potential of FD as a decision-making parameter for dermatological lesion discrimination.

Main Methods:

Analysis of 39,270 dermatological lesions from the International Skin Imaging Collaboration dataset.
Calculation of box-counting fractal dimensions for each lesion.
Implementation of unsupervised machine learning models (PCA, iterated K-means) using FD values.

Main Results:

Fractal dimension values alone showed significant separation between benign/malignant and melanoma/non-melanoma lesions (sensibility ~72%, specificity ~50%).
Unsupervised AI models based on FD achieved classification accuracy of approximately 80% for lesion types.
Classification of metastatic versus non-metastatic melanoma was less effective, likely due to sample size limitations.

Conclusions:

Fractal dimension is a viable metric for developing unsupervised AI models for efficient dermatological lesion classification.
A decision algorithm based on FD can aid in discriminating dermatological lesions.
This approach offers a promising non-invasive strategy for improving melanoma detection and classification.