Unveiling the power of convolutional neural networks in melanoma diagnosis

Unveiling the power of convolutional neural networks in melanoma diagnosis

Background Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpa...

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Bibliographic Details
Published in:EJD. European journal of dermatology Vol. 33; no. 5; pp. 495 - 505
Main Authors: Van Dieren, Loïc, Amar, Jonathan Z., Geurs, Naomi, Quisenaerts, Tom, Gillet, Clément, Delforge, Benoit, De Crane D’Heysselaer, Lara, Thiessen Ef, Filip, Cetrulo, Curtis L., Lellouch, Alexandre G.
Format: Journal Article
Language:English
Published: Paris John Libbey Eurotext 01-10-2023
Subjects:
Algorithms
Artificial Intelligence
Dermatologists
Dermatology
Dermoscopy - methods
Humans
Investigative Report
Medicine
Medicine & Public Health
Melanoma - diagnosis
Melanoma - pathology
Neural Networks, Computer
Skin Neoplasms - diagnosis
Skin Neoplasms - pathology
skin cancer
computer-assisted diagnosis
melanoma
dermatology
artificial intelligence
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Summary:Background Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation. Objectives We describe how a convolutional neural network differentiates a benign from a malignant lesion. Materials & Methods We systematically searched the Web of Science, Medline (PubMed), and The Cochrane Library on the 9 th February, 2022. We focused on articles describing the role and use of artificial intelligence in melanoma recognition between 2017 and 2022, using the following MeSH terms: “melanoma,” “diagnosis,” and “artificial intelligence”. Results Traditional machine learning algorithms comprise different parts which must preprocess, segment, extract features and classify the lesion into benign or malignant. Deep learning algorithms can perform these steps simultaneously, which significantly enhances efficiency. Convolutional neural networks include a convolutional layer, a pooling layer, and a fully connected layer. Convolutional and pooling layers extract features from the lesion and reduce computational power, whereas fully connected layers classify the image into two or more categories. Conclusion Additionally, we suggest that further studies should be performed to accelerate the clinical implementation of artificial intelligence, to create comprehensive datasets and to generate explainable algorithms.
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ISSN:1167-1122
1952-4013
DOI:10.1684/ejd.2023.4559