Deepmole: Deep neural networks for skin mole lesion classification

Pomponiu, Victor; Nejati, Hossein; Cheung, Ngai-Man

doi:10.1109/icip.2016.7532834

Cited by 107 publications

(62 citation statements)

References 16 publications

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“…Pomponiu et al [ 23 ] used only 399 images from a standard camera for the classification of melanomas versus benign nevi. First, data augmentation and preprocessing were performed.…”

Section: Resultsmentioning

confidence: 99%

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Brinker¹,

Hekler²,

Utikal³

et al. 2018

J Med Internet Res

366

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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“…Pomponiu et al [ 23 ] used only 399 images from a standard camera for the classification of melanomas versus benign nevi. First, data augmentation and preprocessing were performed.…”

Section: Resultsmentioning

confidence: 99%

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Brinker¹,

Hekler²,

Utikal³

et al. 2018

J Med Internet Res

366

167

View full text Add to dashboard Cite

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“…We propose to use a supervised Deep Neural Network to determine the tissue types [13]. While training a DNN from scratch requires a significantly large training dataset, recent works have shown that the higher layers of a DNN trained on a large labeled dataset could be general enough for another image classification task (a.k.a.…”

Section: Methodsmentioning

confidence: 99%

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Nejati

Ghazijahani

Abdollahzadeh

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic wound tissue analysis, there has been a shortcoming in these approaches for clinical practices. In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure. In this paper, for the first time, we investigate the classification of 7 wound tissue types. We work with wound professionals to build a new database of 7 types of wound tissue. We propose to use pre-trained deep neural networks for feature extraction and classification at the patch-level. We perform experiments to demonstrate that our approach outperforms other state-of-the-art. We will make our database publicly available to facilitate research in wound assessment.

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“…These studies primarily rely on CNN for image recognition and classification. Initially, a pretrained CNN (i.e., AlexNet) was only used to extract features, and these features were then classified by a more simplistic ML algorithm, such as k-nearest neighbors or SVMs [24][25][26]. Currently, most CNNs can both extract features and classify images through end-to-end learning.…”

Section: Classification Of Dermatological Diseases Using Clinical Imagesmentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

185

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Deepmole: Deep neural networks for skin mole lesion classification

Cited by 107 publications

References 16 publications

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

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