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| Design of a Machine Learning System to Predict the Thickness of a Melanoma Lesion in a Non-Invasive Way from Dermoscopic Images |
| Ádám Szijártó, Ellák Somfai, András Lőrincz |
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Healthc Inform Res. 2023;29(2):112-119. Published online April 30, 2023 DOI: https://doi.org/10.4258/hir.2023.29.2.112 |
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Design of a Machine Learning System to Predict the Thickness of a Melanoma Lesion in a Non-Invasive Way from Dermoscopic Images Healthcare Informatics Research. 2023;29(2):112-119  Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images IEEE Transactions on Medical Imaging. 2016;35(4):1036-1045 A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images . 2021; Deep Learning based Melanoma Detection from Dermoscopic Images 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). 2019; Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification Computation. 2020;8(2):41 An Explainable Convolutional Neural Network‐Based Method for Skin‐Lesion Classification from Dermoscopic Images Machine Learning Algorithms for Signal and Image Processing. 2022;279-291 Diagnosing of Dermoscopic Images using Machine Learning approaches for Melanoma Detection 2020 IEEE 23rd International Multitopic Conference (INMIC). 2020; A Decision Support System for Melanoma Diagnosis from Dermoscopic Images Applied Sciences. 2022;12(14):7007 Detection of Melanoma Skin Cancer using Dermoscopic Skin Lesion Images 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). 2021; Skin lesion classification of dermoscopic images using machine learning and convolutional neural network Scientific Reports. 2022;12(1): |
