Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Bandy, A.D., Spyridis, Y., Villarini, B. and Argyriou, V. 2023. Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma. Sensors. 23 (2), p. 926. https://doi.org/10.3390/s23020926

TitleIntraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma
TypeJournal article
AuthorsBandy, A.D., Spyridis, Y., Villarini, B. and Argyriou, V.
Abstract

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%.

Keywordsclassification
skin lesion clustering
malignant melanoma
machine learning
CNN
medical image processing
JournalSensors
Journal citation23 (2), p. 926
ISSN1424-8220
Year2023
PublisherMDPI
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.3390/s23020926
PubMed ID36679721
Web address (URL)https://doi.org/10.3390/s23020926
Publication dates
Published13 Jan 2023

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