Title | Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma |
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Type | Journal article |
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Authors | Bandy, A.D., Spyridis, Y., Villarini, B. and Argyriou, V. |
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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%. |
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Keywords | classification |
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| skin lesion clustering |
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| malignant melanoma |
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| machine learning |
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| CNN |
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| medical image processing |
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Journal | Sensors |
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Journal citation | 23 (2), p. 926 |
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ISSN | 1424-8220 |
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Year | 2023 |
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Publisher | MDPI |
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Publisher's version | License CC BY 4.0 File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.3390/s23020926 |
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PubMed ID | 36679721 |
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Web address (URL) | https://doi.org/10.3390/s23020926 |
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Publication dates |
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Published | 13 Jan 2023 |
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