Impact of Deep Learning and Smartphone Technologies in Dermatology: Automated Diagnosis

Skin diseases are considered as major public health problems. Early diagnosis of skin diseases is crucial for patients to get the treatment on time. However, several factors make difficult to access medical care. For instance, physical disability, physiological problems, old age, distance, limited medical expertise in rural areas, climate conditions and employment. Also, long diagnosing time and high cost can discourage people from receiving dermatological care.The increasing availability and easy to use of smartphone applications has allowed significant growth of smartphones. Various smartphone applications have been introduced in different areas of medicine. They are becoming important particularly in dermatology since dermatological diseases are usually visible by human eyes and diagnosis is mainly based on visual screening of the lesions and pattern recognition. Therefore, patients can have active roles in their health-management using these applications. However, is it possible to diagnose skin diseases automatically with the advancements in mobile technologies and deep learning-based methodologies? To answer this question, in this paper, recent smartphone applications have been reviewed.

[1]  L. Kruse,et al.  The impact of pediatric skin disease on self-esteem. , 2018, International journal of women's dermatology.

[2]  T. Ruzicka,et al.  mHealth App for Risk Assessment of Pigmented and Nonpigmented Skin Lesions-A Study on Sensitivity and Specificity in Detecting Malignancy. , 2017, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[3]  T. Biedermann,et al.  Skin diseases are more common than we think: screening results of an unreferred population at the Munich Oktoberfest , 2019, Journal of the European Academy of Dermatology and Venereology : JEADV.

[4]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Chris T. Kiranoudis,et al.  Automated skin lesion assessment using mobile technologies and cloud platforms , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Muhammad Ali Ramdhani,et al.  Implementation of Nearest Neighbor using HSV to Identify Skin Disease , 2018 .

[7]  Renato A. Krohling,et al.  The impact of patient clinical information on automated skin cancer detection , 2020, Comput. Biol. Medicine.

[8]  Man Jae Kim.  Development of a Portable Optical Imaging System based on a Smartphone and Image Classification using a Learning Algorithm , 2017 .

[9]  Alexandros Karargyris,et al.  DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[10]  Jinshan Tang,et al.  A mobile system for skin cancer diagnosis and monitoring , 2014, Sensing Technologies + Applications.

[11]  R Saranya,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2019 .

[12]  Ausif Mahmood,et al.  Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.

[13]  Santosh Pandey,et al.  Skin Cancer Diagnostics with an All-Inclusive Smartphone Application , 2019, Symmetry.

[14]  M. W. M. Jaspers,et al.  Two Decades of Teledermatology: Current Status and Integration in National Healthcare Systems , 2016, Current Dermatology Reports.

[15]  Omar Abuzaghleh,et al.  Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention , 2015, ArXiv.

[16]  A. A. Zaidan,et al.  A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution , 2018, Health and Technology.

[17]  Evgin Goceri,et al.  Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network , 2019, International journal for numerical methods in biomedical engineering.

[18]  L. Naldi,et al.  Prevalence of skin disease in a population‐based sample of adults from five European countries , 2018, The British journal of dermatology.

[19]  Nicholas B. MacKinnon,et al.  Vanishing point: a smartphone application that classifies acne lesions and estimates prognosis , 2016 .

[20]  Boreom Lee,et al.  Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis. , 2016, Biomedical optics express.

[21]  L. Naldi.  The Field and Its Boundaries , 2009 .

[22]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[23]  P. Yin,et al.  Burden of melanoma in China, 1990–2017: Findings from the 2017 global burden of disease study , 2020, International journal of cancer.

[24]  Ali Mahloojifar,et al.  A Mobile Application for Early Detection of Melanoma by Image Processing Algorithms , 2018, 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME).

[25]  Yi Shang,et al.  A Mobile Automated Skin Lesion Classification System , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[26]  A. Qureshi,et al.  Factors predictive of recurrence and death from cutaneous squamous cell carcinoma: a 10-year, single-institution cohort study. , 2013, JAMA dermatology.

[27]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[28]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[29]  Ngai-Man Cheung,et al.  Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis , 2017, IEEE Transactions on Multimedia.

[30]  A. Ng,et al.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists , 2018, PLoS medicine.

[31]  Michael Werman,et al.  Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN , 2018, Advances in Intelligent Systems and Computing.

[32]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[33]  E. McCarthy,et al.  The Influence of Dermatologist and Primary Care Physician Visits on Melanoma Outcomes Among Medicare Beneficiaries , 2013, The Journal of the American Board of Family Medicine.

[34]  Alexandru Telea,et al.  An Image Inpainting Technique Based on the Fast Marching Method , 2004, J. Graphics, GPU, & Game Tools.

[35]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[36]  C. Berking,et al.  Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result , 2015, Journal of the European Academy of Dermatology and Venereology : JEADV.

[37]  D. Murrell,et al.  Patient and practitioner satisfaction with tele-dermatology including Australia’s indigenous population: A systematic review of the literature , 2016, International journal of women's dermatology.

[38]  C. Cockerell,et al.  Actinic Keratoses , 1997 .

[39]  August Thio-ac,et al.  A Smartphone-Based Skin Disease Classification Using MobileNet CNN , 2019, International Journal of Advanced Trends in Computer Science and Engineering.

[40]  Reza Fazel-Rezai,et al.  Automatic diagnosis of melanoma using linear and nonlinear features from digital image , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[41]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[42]  B. Dréno,et al.  Patients at elevated risk of melanoma: individual predictors of non-compliance to GP referral for a dermatologist consultation. , 2014, Preventive medicine.

[43]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[44]  Borko Furht,et al.  Cloud-Based Skin Lesion Diagnosis System Using Convolutional Neural Networks , 2019, Advances in Intelligent Systems and Computing.

[45]  J. English,et al.  Teledermatology: A Review and Update , 2018, American Journal of Clinical Dermatology.

[46]  D. Margolis,et al.  The burden of skin disease in the United States , 2017, Journal of the American Academy of Dermatology.

[47]  Mohammed Misbhauddin,et al.  Deep Neural Network Based Mobile Dermoscopy Application for Triaging Skin Cancer Detection , 2019, 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS).

[48]  J. Resneck,et al.  Choice, Transparency, Coordination, and Quality Among Direct-to-Consumer Telemedicine Websites and Apps Treating Skin Disease. , 2016, JAMA dermatology.

[49]  B. B. Zaidan,et al.  A Systematic Review on Smartphone Skin Cancer Apps: Coherent Taxonomy, Motivations, Open Challenges and Recommendations, and New Research Direction , 2017, J. Circuits Syst. Comput..

[50]  H. Williams,et al.  Teledermatology for diagnosing skin cancer in adults. , 2018, The Cochrane database of systematic reviews.

[51]  Wiphada Wettayaprasit,et al.  Convolutional Neural Networks Using MobileNet for Skin Lesion Classification , 2019, 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[52]  Nikos Petrellis.  Skin disorder diagnosis assisted by lesion color adaptation , 2018, PCI.

[53]  H. Peter Soyer,et al.  Fighting Melanoma with Smartphones: A Snapshot of Where We are a Decade after App Stores Opened Their Doors , 2018, Int. J. Medical Informatics.

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

[55]  Adeel Anjum,et al.  m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine , 2016, eHealth 360°.

[56]  Rosepreet Kaur Bhogal,et al.  Serving the Dermatologists: Skin Diseases Detection , 2019, Information and Communication Technology for Sustainable Development.