Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Filippo Molinari,et al.  Quantitative analysis of patellar tendon abnormality in asymptomatic professional "Pallapugno" players: a texture-based ultrasound approach , 2018 .

[3]  Amira S. Ashour,et al.  A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification , 2018, Comput. Methods Programs Biomed..

[4]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[5]  Jordan V. Wang,et al.  Challenges to smartphone applications for melanoma detection. , 2017, Dermatology online journal.

[6]  John Paoli,et al.  Mobile teledermoscopy—there’s an app for that! , 2013, Dermatology practical & conceptual.

[7]  David A. Belsley,et al.  Regression Analysis and its Application: A Data-Oriented Approach.@@@Applied Linear Regression.@@@Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1981 .

[8]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[9]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[10]  J. Paoli,et al.  The Euromelanoma skin cancer prevention campaign in Europe: characteristics and results of 2009 and 2010 , 2011, Journal of the European Academy of Dermatology and Venereology : JEADV.

[11]  J. Kvedar,et al.  Teledermatology: from historical perspective to emerging techniques of the modern era: part II: Emerging technologies in teledermatology, limitations and future directions. , 2015, Journal of the American Academy of Dermatology.

[12]  R. Hofmann-Wellenhof,et al.  Mobile teledermatology for skin tumour screening: diagnostic accuracy of clinical and dermoscopic image tele‐evaluation using cellular phones , 2011, The British journal of dermatology.