eISSN: 2299-0046
ISSN: 1642-395X
Advances in Dermatology and Allergology/Postępy Dermatologii i Alergologii
Current issue Archive Manuscripts accepted About the journal Editorial board Journal's reviewers Abstracting and indexing Subscription Contact Instructions for authors
SCImago Journal & Country Rank
3/2021
vol. 38
 
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abstract:
Original paper

Convolutional neural networks for the detection of malignant melanoma in dermoscopy images

Dominika Kwiatkowska
1
,
Piotr Kluska
2
,
Adam Reich
1

1.
Department of Dermatology, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszow, Poland
2.
Wroclaw University of Science and Technology, Wrocław, Poland
Adv Dermatol Allergol 2021; XXXVIII (3): 412–420
Online publish date: 2021/07/26
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Introduction
Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images.

Aim
To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images.

Material and methods
Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity, F1 score, specificity and area under the receiver operating curve were calculated. In addition, an ensemble of all neural networks’ ability of proper malignant melanoma classification was compared with the results achieved by every single network.

Results
The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types.

Conclusions
Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images.

keywords:

dermoscopy, melanoma, neural networks, deep learning

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