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Article

Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures

by
Aline Lissa Okita
1,*,
Raquel Machado de Sousa
1,
Eddy Jens Rivero-Zavala
1,
Karina Lumy Okita
1,
Luisa Juliatto Molina Tinoco
1,
Luis Eduardo Pedigoni Bulisani
2 and
Andre Pires dos Santos
1
1
Centro de Pesquisa em Imagem, Hospital Israelita Albert Einstein, Av. Albert Einstein, 627-Jardim Leonor, São Paulo 05652-900, SP, Brazil
2
Faculdade de Medicina de Jundiaí, Rua Francisco Telles, 250, Vila Arens, Jundiaí 13202-550, SP, Brazil
*
Author to whom correspondence should be addressed.
Dermato 2024, 4(3), 97-111; https://doi.org/10.3390/dermato4030011
Submission received: 8 June 2024 / Revised: 2 August 2024 / Accepted: 12 August 2024 / Published: 16 August 2024
(This article belongs to the Collection Artificial Intelligence in Dermatology)
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Abstract

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has recently made great advances in dermatology with respect to the classification and malignancy prediction of skin diseases. In this article, we demonstrate how we have used a similar technique to build a mobile application to classify skin diseases captured by patients with their personal smartphone cameras. We used a CNN classifier to distinguish four subtypes of dermatological diseases the patients might have (“pigmentation changes and superficial infections”, “inflammatory diseases and eczemas”, “benign tumors, cysts, scars and callous”, and “suspected lesions”) and their severity in terms of morbidity and mortality risks, as well as the kind of medical consultation the patient should seek. The dataset used in this research was collected by the Department of Telemedicine of Albert Einstein Hospital in Sao Paulo and consisted of 146.277 skin images. In this paper, we show that our CNN models with an overall average classification accuracy of 79% and a sensibility of above 80% implemented in personal smartphones have the potential to lower the frequency of skin diseases and serve as an advanced tracking tool for a patient’s skin-lesion history.
Keywords: artificial intelligence; dermatology; machine learning; deep learning artificial intelligence; dermatology; machine learning; deep learning

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MDPI and ACS Style

Okita, A.L.; de Sousa, R.M.; Rivero-Zavala, E.J.; Okita, K.L.; Molina Tinoco, L.J.; Bulisani, L.E.P.; dos Santos, A.P. Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures. Dermato 2024, 4, 97-111. https://doi.org/10.3390/dermato4030011

AMA Style

Okita AL, de Sousa RM, Rivero-Zavala EJ, Okita KL, Molina Tinoco LJ, Bulisani LEP, dos Santos AP. Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures. Dermato. 2024; 4(3):97-111. https://doi.org/10.3390/dermato4030011

Chicago/Turabian Style

Okita, Aline Lissa, Raquel Machado de Sousa, Eddy Jens Rivero-Zavala, Karina Lumy Okita, Luisa Juliatto Molina Tinoco, Luis Eduardo Pedigoni Bulisani, and Andre Pires dos Santos. 2024. "Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures" Dermato 4, no. 3: 97-111. https://doi.org/10.3390/dermato4030011

APA Style

Okita, A. L., de Sousa, R. M., Rivero-Zavala, E. J., Okita, K. L., Molina Tinoco, L. J., Bulisani, L. E. P., & dos Santos, A. P. (2024). Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures. Dermato, 4(3), 97-111. https://doi.org/10.3390/dermato4030011

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