REFERENCES:
1.
Nami N, Giannini E, Burroni M, Fimiani M, Rubegni P.
Teledermatology: state-of-the-art and future perspectives. Expert Review of
Dermatology. 2012 1;7(1):1-3.
2.
Fabbrocini G, Triassi M, Mauriello
MC, Torre G, Annunziata MC, De Vita V, Pastore F, D’Arco V, Monfrecola G.
Epidemiology of skin cancer: role of some environmental factors. Cancers. 2010;2(4):1980-1989.
3.
Esteva A, Kuprel B, Novoa RA, Ko J,
Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer
with deep neural networks. Nature. 2017;542(7639):115.
4.
Ali AR, Deserno TM. A systematic
review of automated melanoma detection in dermatoscopic images and its ground
truth data. InMedical Imaging 2012: Image Perception, Observer Performance, and
Technology Assessment 2012 ; 8318 - 83181I)
5.
Kittler H, Pehamberger H, Wolff K,
Binder M. Diagnostic accuracy of dermoscopy. The lancet oncology. 20020;3(3):159-65.
6.
Ali AR, Deserno TM. A systematic
review of automated melanoma detection in dermatoscopic images and its ground
truth data. InMedical Imaging 2012: Image Perception, Observer Performance, and
Technology Assessment 2012 ; 8318 -
83181.
7.
Fabbrocini G, De Vita V, Pastore F,
D'Arco V, Mazzella C, Annunziata MC, Cacciapuoti S, Mauriello MC, Monfrecola A.
Teledermatology: from prevention to diagnosis of nonmelanoma and melanoma skin
cancer. International journal of telemedicine and applications. 2011
8.
Menegola A, Fornaciali M, Pires R,
Bittencourt FV, Avila S, Valle E. Knowledge transfer for melanoma screening
with deep learning. In2017 IEEE 14th International Symposium on Biomedical
Imaging (ISBI 2017) 2017 ; 18: 297-300.
9.
Pomponiu V, Nejati H, Cheung NM.
Deepmole: Deep neural networks for skin mole lesion classification. In2016 IEEE
International Conference on Image Processing (ICIP) 2016 ;25: 2623-2627.
10.
Codella N, Cai J, Abedini M, Garnavi
R, Halpern A, Smith JR. Deep learning, sparse coding, and SVM for melanoma
recognition in dermoscopy images. InInternational workshop on machine learning
in medical imaging 2015 Oct 5 (pp. 118-126). Springer, Cham.
11.
Kawahara J, BenTaieb A, Hamarneh G.
Deep features to classify skin lesions. In2016 IEEE 13th International Symposium
on Biomedical Imaging (ISBI) 2016 : 1397-1400.
12.
Esteva A, Kuprel B, Novoa RA, Ko J,
Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer
with deep neural networks. Nature. 2017; 542(7639):115.
13.
Han SS, Kim MS, Lim W, Park GH, Park
I, Chang SE. Classification of the clinical images for benign and malignant
cutaneous tumors using a deep learning algorithm. Journal of Investigative
Dermatology. 2018 1;138(7):1529-38.
14.
Bi L, Kim J, Ahn E, Feng D.
Automatic skin lesion analysis using large-scale dermoscopy images and deep
residual networks. arXiv preprint arXiv:1703.04197. 2017 :1-2.
15.
Kawahara J, Hamarneh G.
Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained
layers. InInternational Workshop on Machine Learning in Medical Imaging 2016 :
164-171.
16.
Navarrete-Dechent C, Dusza SW,
Liopyris K, Marghoob AA, Halpern AC, Marchetti MA. Automated dermatological
diagnosis: hype or reality?. The Journal of investigative dermatology.
2018;138(10):2277.
17.
Shin HC, Roth HR, Gao M, Lu L, Xu Z,
Nogues I, Yao J, Mollura D, Summers RM. Deep convolutional neural networks for
computer-aided detection: CNN architectures, dataset characteristics and
transfer learning. IEEE transactions on medical imaging. 2016 11;35(5):1285-98.
18.
Sharif Razavian A, Azizpour H,
Sullivan J, Carlsson S. CNN features off-the-shelf: an astounding baseline for
recognition. InProceedings of the IEEE conference on computer vision and
pattern recognition workshops :. 806-813.
19.
Zhou B, Lapedriza A, Xiao J,
Torralba A, Oliva A. Learning deep features for scene recognition using places
database. InAdvances in neural information processing systems 2014 : 487-495.
20.
Szegedy C, Vanhoucke V, Ioffe S,
Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. InProceedings
of the IEEE conference on computer vision and pattern recognition 2016 :
2818-2826.
21.
Szegedy C, Liu W, Jia Y, Sermanet P,
Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with
convolutions. InProceedings of the IEEE conference on computer vision and
pattern recognition 2015 : 1-9).
22.
Lin M, Chen Q, Yan S. Network in
network. arXiv preprint arXiv:1312.4400. 2013 16.
23. Szegedy
C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-resnet and the
impact of residual connections on learning. InThirty-First AAAI Conference on Artificial
Intelligence 2017 12.