Classification of Melanoma Presence and Thickness Based on Computational Image Analysis

Sánchez‐Monedero, Javier; Sáez, Aurora; Pérez-Ortiz, María; Gutiérrez, Pedro Antonio; Hervás-Martı́nez, César

doi:10.1007/978-3-319-32034-2_36

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Some of the extracted features are based on several previous works that the reader can check for more details [11,38,22]. Moreover, in the current study, we include 14 additional shape and colour features.…”

Section: Data and Breslow Index Descriptionmentioning

confidence: 99%

Partial order label decomposition approaches for melanoma diagnosis

Sánchez‐Monedero

Pérez-Ortiz

Sáez

et al. 2018

Applied Soft Computing

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Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining

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Section: Data and Breslow Index Descriptionmentioning

confidence: 99%

Partial order label decomposition approaches for melanoma diagnosis

Sánchez‐Monedero

Pérez-Ortiz

Sáez

et al. 2018

Applied Soft Computing

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“…In a follow-up paper [10], the same techniques were applied to five classes. In addition to the three melanoma thickness classes, the study also incorporated non-melanoma and melanoma in situ images.…”

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confidence: 99%

Design of a Machine Learning System to Predict the Thickness of a Melanoma Lesion in a Non-Invasive Way from Dermoscopic Images

2023

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Objectives: Melanoma is the deadliest form of skin cancer, but it can be fully cured through early detection and treatment in 99% of cases. Our aim was to develop a non-invasive machine learning system that can predict the thickness of a melanoma lesion, which is a proxy for tumor progression, through dermoscopic images. This method can serve as a valuable tool in identifying urgent cases for treatment.Methods: A modern convolutional neural network architecture (EfficientNet) was used to construct a model capable of classifying dermoscopic images of melanoma lesions into three distinct categories based on thickness. We incorporated techniques to reduce the impact of an imbalanced training dataset, enhanced the generalization capacity of the model through image augmentation, and utilized five-fold cross-validation to produce more reliable metrics.Results: Our method achieved 71% balanced accuracy for three-way classification when trained on a small public dataset of 247 melanoma images. We also presented performance projections for larger training datasets.Conclusions: Our model represents a new state-of-the-art method for classifying melanoma thicknesses. Performance can be further optimized by expanding training datasets and utilizing model ensembles. We have shown that earlier claims of higher performance were mistaken due to data leakage during the evaluation process.

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