Hybrid deep learning for Reflectance Confocal Microscopy skin images

Kaur, Parneet; Dana, Kristin; Cula, Gabriela Oana; Mack, M Catherine

doi:10.1109/icpr.2016.7899844

Cited by 21 publications

(24 citation statements)

References 22 publications

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“…Deep learning methods by Bozkurt et al 16 and Kaur et al 12 seem to also take into account the dependencies between images to perform the classification, the author did not observe any incoherent transitions, but the regression might lack interpretability. We have proposed a method to segment the DEJ in confocal microscopy images.…”

Section: Comparison To State-of-the-art Methodsmentioning

confidence: 99%

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Three-dimensional conditional random field for the dermal–epidermal junction segmentation

et al. 2019

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The segmentation of the dermal-epidermal junction (DEJ) in in vivo confocal images represents a challenging task due to uncertainty in visual labeling and complex dependencies between skin layers. We propose a method to segment the DEJ surface, which combines random forest classification with spatial regularization based on a three-dimensional conditional random field (CRF) to improve the classification robustness. The CRF regularization introduces spatial constraints consistent with skin anatomy and its biological behavior. We propose to specify the interaction potentials between pixels according to their depth and their relative position to each other to model skin biological properties. The proposed approach adds regularity to the classification by prohibiting inconsistent transitions between skin layers. As a result, it improves the sensitivity and specificity of the classification results.

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Section: Comparison To State-of-the-art Methodsmentioning

confidence: 99%

“…Kaur et al 12 used a hybrid of classical methods in texture recognition and recent deep learning methods, which gives good results on a moderate size database of 15 stacks. They classify each confocal image as one of the skin layers.…”

Section: Related Workmentioning

confidence: 99%

Three-dimensional conditional random field for the dermal–epidermal junction segmentation

et al. 2019

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“…The majority of the published algorithms described automated methods of stratifying skin layers in RCM images by labeling each of the epidermal strata in groups from the stratum corneum to the basal layer [12,[24][25][26]43,44,46] and delineating the DEJ from the epidermis and dermis [47][48][49][50][51][52][53][54][55]. Table 2 summarizes the methods described for epidermal stratification.…”

Section: Skin Stratificationmentioning

confidence: 99%

“…Sensitivity and specificity were maximized with these techniques as well, achieving 87% and 94%, respectively [12,44]. Algorithms stratifying the epidermal layers with texture and feature analysis combined with logistic regression, conditional random fields, or support vector machines (SVMs) [24,25,43] were found to have inferior accuracies, sensitivities, and specificities when compared with neural network-based algorithms [12,26,44]. One algorithm attempting to delineate only the stratum corneum using texture analysis and wavelet transformation performed at 82.89% accuracy compared to ground-truth expert labeling of slices [46].…”

Section: Skin Stratificationmentioning

confidence: 99%

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

et al. 2021

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Background and Objectives Non‐invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near‐infrared laser light to enable real‐time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time‐consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging. Study Design/Materials and Methods A systematic PubMed search was conducted with additional relevant literature obtained from reference lists. Results Algorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers. Conclusions AI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC

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“…Kawahara et al [16] used convolution neural network (CNN) early on to classify dermatologic images and achieved good results. Kaur et al [17] demonstrated that the classification accuracy of hybrid deep learning method could reach up to 82.0% among six kinds of skin images, which was better than the traditional CNN models. Romero-Lopez et al [18] exploited VGGNet to classify benign and malignant types of skin cancer and achieved a classification accuracy of 78.66% based on the International Skin Imaging Collaboration (ISIC) dataset.…”

Section: Introductionmentioning

confidence: 99%

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

et al. 2020

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Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

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Hybrid deep learning for Reflectance Confocal Microscopy skin images

Cited by 21 publications

References 22 publications

Three-dimensional conditional random field for the dermal–epidermal junction segmentation

Three-dimensional conditional random field for the dermal–epidermal junction segmentation

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

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