[1]
National Cancer Institute Services, what you need to know about melanoma and other skin cancers. NIH Publication No.10-7625, Revised June 2010, Printed October (2010).
Google Scholar
[2]
Fabbrocini, Gabriella, et al. Epidemiology of skin cancer: role of some environmental factors., Cancers 2.4 (2010): 1980-1989.
Google Scholar
[3]
Brinker, Titus Josef, et al. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review., Journal of medical Internet research 20.10 (2018).
Google Scholar
[4]
Ali, Abder-Rahman A., and Thomas M. Deserno. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data., Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment. Vol. 8318. International Society for Optics and Photonics, (2012).
DOI: 10.1117/12.912389
Google Scholar
[5]
Alqudah, Ali Mohammad, et al. Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images., Journal of Biomimetics, Biomaterials and Biomedical Engineering. Vol. 39. Trans Tech Publications, (2018).
DOI: 10.4028/www.scientific.net/jbbbe.39.40
Google Scholar
[6]
Alqudah, Ali Mohammad, et al. COMPUTER AIDED DIAGNOSIS SYSTEM FOR AUTOMATIC TWO STAGES CLASSIFICATION OF BREAST MASS IN DIGITAL MAMMOGRAM IMAGES., Biomedical Engineering: Applications, Basis and Communications (2018): 1950007.
DOI: 10.4015/s1016237219500078
Google Scholar
[7]
Wernick, Miles N., et al. Machine learning in medical imaging., IEEE signal processing magazine 27.4 (2010): 25-38.
Google Scholar
[8]
Alquran, Hiam, et al. The melanoma skin cancer detection and classification using support vector machine., Applied Electrical Engineering and Computing Technologies (AEECT), 2017 IEEE Jordan Conference on. IEEE, 2017.
DOI: 10.1109/aeect.2017.8257738
Google Scholar
[9]
Hoffmann, Klaus, et al. Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer‐aided analysis of data from pigmented skin lesions using digital dermoscopy., British Journal of Dermatology149.4 (2003): 801-809.
DOI: 10.1046/j.1365-2133.2003.05547.x
Google Scholar
[10]
Li, Yuexiang, and Linlin Shen. Skin lesion analysis towards melanoma detection using deep learning network., Sensors 18.2 (2018): 556.
DOI: 10.3390/s18020556
Google Scholar
[11]
Shoieb, Doaa A., Sherin M. Youssef, and Walid M. Aly. Computer-aided model for skin diagnosis using deep learning., Journal of Image and Graphics 4.2 (2016).
DOI: 10.18178/joig.4.2.122-129
Google Scholar
[12]
Premaladha, J., and K. S. Ravichandran. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms., Journal of medical systems 40.4 (2016): 96.
DOI: 10.1007/s10916-016-0460-2
Google Scholar
[13]
Lopez, Adria Romero, et al. Skin lesion classification from dermoscopic images using deep learning techniques., Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on. IEEE, (2017).
DOI: 10.2316/p.2017.852-053
Google Scholar
[14]
Bi, Lei, et al. Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks., arXiv preprint arXiv:1703.04197 (2017).
Google Scholar
[15]
Demyanov, Sergey, et al. Classification of dermoscopy patterns using deep convolutional neural networks., Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. IEEE, (2016).
DOI: 10.1109/isbi.2016.7493284
Google Scholar
[16]
ISIC Archive. https://www.isic-archive.com. Access at 19 March. (2018).
Google Scholar
[17]
Szegedy, Christian, et al. Going deeper with convolutions., Proceedings of the IEEE conference on computer vision and pattern recognition. (2015).
Google Scholar
[18]
Russakovsky, Olga, et al. Imagenet large scale visual recognition challenge., International Journal of Computer Vision115.3 (2015): 211-252.
Google Scholar
[19]
zegedy, Christian, et al. Inception-v4, inception-resnet and the impact of residual connections on learning., AAAI. Vol. 4. (2017).
DOI: 10.1609/aaai.v31i1.11231
Google Scholar
[20]
Hoo-Chang, Shin, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning., IEEE transactions on medical imaging 35.5 (2016): 1285.
DOI: 10.1109/tmi.2016.2528162
Google Scholar
[21]
Goyal, Manu, and Moi Hoon Yap. Multi-class semantic segmentation of skin lesions via fully convolutional networks., arXiv preprint arXiv:1711.10449 (2017).
DOI: 10.5220/0009380302900295
Google Scholar
[22]
Alom, Md Zahangir, et al. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches., arXiv preprint arXiv:1803.01164 (2018).
Google Scholar
