Epiluminescence Image Processing for Melanocytic Skin Lesion Diagnosis Based on 7-Point Check-List: A Preliminary Discussion on Three Parameters

Fabbrocini, Gabriella; Betta, G.; Leo, Giuseppe Di; Liguori, Consolatina; Paolillo, Alfredo; Pietrosanto, Antonio; Sommella, Paolo; Rescigno, Orsola; Cacciapuoti, Sara; Pastore, Fiorella; Vita, Valerio De; Mordente, Ines; Ayala, Fabio

doi:10.2174/1874372201004010110

Cited by 26 publications

(11 citation statements)

References 21 publications

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“…Pattern analysis and assessment of the macroscopic image showed the highest specificity, 85.3% and 85.4%, respectively. So many researchers [ 109 – 114 ] are trying to develop efficient automatic diagnostic systems based on 7-point criteria and pattern analysis.…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

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Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

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Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

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“…Automated lesion classification can both support physicians in their daily clinical routine and enable fast and cheap access to lifesaving diagnoses, even outside the hospital, through installation of apps on mobile devices [ 8 , 9 ]. Before 2016, research mostly followed the classical workflow of machine learning: preprocessing, segmentation, feature extraction, and classification [ 9 - 11 ]. However, a high level of application-specific expertise is required, particularly for feature extraction, and the selection of adequate features is very time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Brinker¹,

Hekler²,

Utikal³

et al. 2018

J Med Internet Res

354

167

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BackgroundState-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area.ObjectiveThis study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future.MethodsWe searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review.ResultsWe found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets.ConclusionsCNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.

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“…Classifying lesions of the skin has been an aim of the machine learning community for some time. Automated classification of lesions is used in clinical examination to help physicians and allow rapid and affordable access to lifesaving diagnoses [40], and outside of the hospital environment, smartphone apps have been used [41]. Before 2016, most research adopted the traditional machine learning workflow of preprocessing (enhancement), segmentation, feature extraction, and classification [41][42][43].…”

mentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Epiluminescence Image Processing for Melanocytic Skin Lesion Diagnosis Based on 7-Point Check-List: A Preliminary Discussion on Three Parameters

Cited by 26 publications

References 21 publications

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

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