Benchmarking convolutional neural networks for diagnosing Lyme disease from images

Lyme disease is one of the most common infectious vector-borne diseases in the world. In the early stage, the disease manifests itself in most cases with erythema migrans (EM) skin lesions. Better diagnosis of these early forms would allow improving the prognosis by preventing the transition to a severe late form thanks to appropriate antibiotic therapy. Recent studies show that convolutional neural networks (CNNs) perform very well to identify skin lesions from the image but, there is not much work for Lyme disease prediction from EM lesion images. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architecture for the purpose. There is no publicly available EM image dataset for Lyme disease prediction mainly because of privacy concerns. In this study, we utilized an EM dataset consisting of images collected from Clermont-Ferrand University Hospital Center (CF-CHU) of France and the internet. CF-CHU collected the images from several hospitals in France. This dataset was labeled by expert dermatologists and

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Eduardo Valle,et al.  Data Augmentation for Skin Lesion Analysis , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[3]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[4]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[5]  William Paul,et al.  AI-based detection of erythema migrans and disambiguation against other skin lesions , 2020, Comput. Biol. Medicine.

[6]  E P Dekonenko,et al.  [The clinical manifestations and diagnosis of Lyme borreliosis]. , 1989, Terapevticheskii arkhiv.

[7]  M. Friedman.  A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[8]  H. Haenssle,et al.  Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[9]  A. Lindberg,et al.  An epidemiologic study of Lyme disease in southern Sweden. , 1995, The New England journal of medicine.

[10]  William H. Sanders,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2014 .

[11]  M. Johnson.  On teaching dermatology to nondermatologists. , 1994, Archives of dermatology.

[12]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  A. Enk,et al.  Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. , 2019, European journal of cancer.

[14]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[15]  R. Hofmann-Wellenhof,et al.  Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. , 2019, The Lancet. Oncology.

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  Woohyung Lim,et al.  Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network , 2018, PloS one.

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

[19]  G. Moreno,et al.  Prospective study to assess general practitioners' dermatological diagnostic skills in a referral setting , 2007, The Australasian journal of dermatology.

[20]  Vera Maraspin Carman,et al.  Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis , 2014 .

[21]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[22]  D. Federman,et al.  Comparison of dermatologic diagnoses by primary care practitioners and dermatologists. A review of the literature. , 1999, Archives of family medicine.

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

[24]  D. Seth,et al.  Global Burden of Skin Disease: Inequities and Innovations , 2017, Current Dermatology Reports.

[25]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Yading Yuan,et al.  Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance , 2017, IEEE Transactions on Medical Imaging.

[27]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[28]  Paul L. Rosin,et al.  Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  E. Shapiro.  Clinical practice. Lyme disease. , 2014, The New England journal of medicine.

[30]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Kai Wang,et al.  A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images , 2016, ECCV.

[32]  Fabio A. González,et al.  A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , 2013, MICCAI.

[33]  H. Feng,et al.  Comparison of Dermatologist Density Between Urban and Rural Counties in the United States , 2018, JAMA dermatology.

[34]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Sebastián Ventura,et al.  Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study , 2020, Medical Image Anal..

[36]  Alfredo Vellido,et al.  The importance of interpretability and visualization in machine learning for applications in medicine and health care , 2019, Neural Computing and Applications.

[37]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[38]  Quoc V. Le,et al.  Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[39]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[41]  F. Strle,et al.  Characterisation of Borrelia burgdorferi sensu lato strains isolated from patients with skin manifestations of Lyme borreliosis residing in Slovenia. , 2000, Journal of medical microbiology.

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

[43]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[44]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  D. Ramsay,et al.  Primary care in dermatology: whose role should it be? , 1996, Journal of the American Academy of Dermatology.

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

[47]  H. Tran,et al.  Assessing diagnostic skill in dermatology: A comparison between general practitioners and dermatologists , 2005, The Australasian journal of dermatology.

[48]  Philippe Burlina,et al.  Skin Image Analysis for Erythema Migrans Detection and Automated Lyme Disease Referral , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[49]  Bo Chen,et al.  MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[51]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[52]  Bo Chen,et al.  NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications , 2018, ECCV.

[53]  J. Resneck,et al.  The dermatology workforce shortage. , 2004, Journal of the American Academy of Dermatology.

[54]  Andreas Holzinger,et al.  Biomedical image augmentation using Augmentor , 2019, Bioinform..

[55]  D. Chicco,et al.  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.

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

[57]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[58]  A. Steere,et al.  The emergence of Lyme disease. , 2004, The Journal of clinical investigation.

[59]  D. Federman,et al.  The abilities of primary care physicians in dermatology: implications for quality of care. , 1997, The American journal of managed care.

[60]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[61]  Achim Hekler,et al.  Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. , 2019, European journal of cancer.

[62]  Rainer Hofmann-Wellenhof,et al.  A deep learning system for differential diagnosis of skin diseases , 2019, Nature Medicine.