Adegun A., Viriri S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8, 2020, 7160–7172.
DOI: https://doi.org/10.1109/ACCESS.2019.2962812
Aggarwal A., Kumar M.: Image surface texture analysis and classification using deep learning, Multimedia Tools and Applications 80, 2021, 1289–1309.
DOI: https://doi.org/10.1007/s11042-020-09520-2
Almeida M., Santos I.: Classification models for skin tumor detection using texture analysis in medical images, J. Imaging 6(51), 2020, [http://doi.org/10.3390/jimaging6060051].
DOI: https://doi.org/10.3390/jimaging6060051
Attia M., Hossny M., Nahavandi S., Yazdabadi A.: Skin melanoma segmentation using recurrent and convolutional neural networks. Biomedical Imaging (ISBI 2017), IEEE 14th International Symposium, 2017, 292–296.
DOI: https://doi.org/10.1109/ISBI.2017.7950522
Barata C., Celebi M., Marques J.: A survey of feature extraction in dermoscopy image analysis of skin cancer. IEEE Journal of Biomedical and Health Informatics 23(3), 2019, 1096–1109.
DOI: https://doi.org/10.1109/JBHI.2018.2845939
Brinker T. J., Hekler A., Enk A. H., Klode J., Hauschild A., Berking C.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113, 2019, 47–54.
Brownlee J.: Gentle introduction to the adam optimization algorithm for deep learning. Machine Learning Mastery Pty. Ltd., 2019, https://machinelearningmastery.com/adamoptimization-algorithm-for-deep-learning/ss
Codella N., Cai J., Abedini M., Garnavi R., Halpern A., Smith J. R.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. International Workshop on Machine Learning in Medical Imaging, 2015, 118–126.
DOI: https://doi.org/10.1007/978-3-319-24888-2_15
Codella N., Nguyen Q., Pankanti S., Gutman D., Helba B., Halpern A., Smith J.: Deep learning ensembles for melanoma recognition in dermoscopy images. arXiv preprint arXiv:1610.04662, 2016.
Esteva A.: Dermatologist-level classification of skin cancer with deep neural networks. Nat. Res. 542(7639), 2017, 115–118.
DOI: https://doi.org/10.1038/nature21056
Ge Y., Li B., Zhao Y., Guan E., Yan W.: Melanoma seg-mentation and classification in clinical images using deep learning, ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018, 252–256.
DOI: https://doi.org/10.1145/3195106.3195164
Ge Z., Demyanov S., Chakravorty R., Bowling A., Garnavi R.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D. L., Duchesne S. (eds.), Springer, Cham LNCS 10435, 250–258, 2017.
DOI: https://doi.org/10.1007/978-3-319-66179-7_29
Gupta A., Thakur S., Rana A.: Study of Melanoma Detection and Classification Techniques. 8th International Conference on Reliability, Infocom Technologies and Optimization, 2020 1345–1350, [http://doi.org/10.1109/ICRITO48877.2020.9197820].
DOI: https://doi.org/10.1109/ICRITO48877.2020.9197820
Haenssle H. A., Fink C., Schneiderbauer R., Toberer F., Buhl T., Blum A.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29, 2018, 1836–1342.
Hekler A., Utikal J. S., Enk A. H., Solass W., Schmitt M., Klode J.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer 118, 2019, 91–96.
DOI: https://doi.org/10.1016/j.ejca.2019.06.012
Hijazi S., Kumar R., Rowen C.: Using convolutional neural networks for image recognition. Cadence Design Systems Inc., San Jose 2015.
Katapadi A. B.: Evolving strategies for the development and evaluation of a computerised melanoma image analysis system. Comput.Methods Biomech. Biomed. Eng., Imag Visual. 6, 2018, 465–472.
DOI: https://doi.org/10.1080/21681163.2016.1277785
Li Y., Shen L.: Skin Lesion Analysis towards Melanoma Detec-tion Using Deep Learning Network. arXiv.org > cs > arXiv:1703.00577, Computer Vision and Pattern Recognition 2017 (v2).
DOI: https://doi.org/10.3390/s18020556
Lopez A. R., Giro-i-Nieto X., Burdick J., Marques O.: Skin lesion classification from dermatoscopic images using deep learning techniques, [http://doi.org/JO.23J6/P.20l7.852-053].
Lopez A. R.: Skin lesion classification from dermoscopic images using deep learning techniques. Proc. 13th IASTED Int. Conf. Biomed. Eng. 2017, 49–54.
Majumder S., Ahsan Ullah M.: Feature extraction from der-moscopy images for an effective diagnosis of melanoma skin cancer. 10th International Conference on Electrical and Compu-ter Engineering Bangladesh, 2018, 185–188.
DOI: https://doi.org/10.1109/ICECE.2018.8636712
Marchetti M. A., Codella N. C., Dusza S. W., Gutman D. A., Helba B., Kalloo A.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78, 2018, 270–277.
DOI: https://doi.org/10.1016/j.jaad.2017.08.016
Marchetti M. A., Liopyris K., Dusza S. W., Codella N. C. F., Gutman D. A., Helba B.: Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol 82, 2020, 622–627.
DOI: https://doi.org/10.1016/j.jaad.2019.07.016
Maron R. C., Weichenthal M., Utikal J. S., Hekler A., Berking C., Hauschild A.: Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 119, 2019, 57–65.
Mendoza C. S., Serrano C., Acha B.: Scale invariant descriptors inpattern analysis of melanocytic lesions. Proc. IEEE 16th Int. Conf. Image Process., 2009, 4193–4196.
DOI: https://doi.org/10.1109/ICIP.2009.5414525
Murphree D. H., Puri P., Shamim H., Bezalel S. A., Drage L. A., Wang M., Pittelkow M. R., Carter R. E., Davis M., Bridges A., Mangold A., Yiannias J., Tollefson M., Lehman J., Meves A., Otley C., Sokumbi O., Hall M., Comfere N.: Deep learning for dermatologists: Part I. J Am Acad Dermatol, 1–9, 2020.
DOI: https://doi.org/10.1016/j.jaad.2020.05.056
Nachbar F., Stolz W., Merkle T., Cognetta A., Vogt T., Landthaler M.: The abcd rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of the American Academy of Dermatology 30(4), 1994, 551–559.
DOI: https://doi.org/10.1016/S0190-9622(94)70061-3
Nida N., Irtaza A., Javed A., Yousaf M., Mahmood M.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics 124, 2019, 37–48.
DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.005
Panja A., Jackson J. Ch., Quadir Md. A.: An Approach to Skin Cancer Detection Using Keras and Tensorflow. Journal of Physics: Conference Series 1911 012032, 2021, [http://doi.org/10.1088/1742-6596/1911/1/012032].
DOI: https://doi.org/10.1088/1742-6596/1911/1/012032
Rahi M., Khan F., Mahtab M., Amanat Ullah A., Alam M. G., Alam M.: Detection Of Skin Cancer Using Deep Neural Networks, IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2019, 1–7, [http://doi.org/10.1109/CSDE48274.2019.9162400].
DOI: https://doi.org/10.1109/CSDE48274.2019.9162400
Romero Lopez A., Xiro-i-Nieto X., Burdick J., Marques O.: Skin lesion classification from dermoscopic images using deep learning techniques. 13th IASTED International Conference on Biomedical Engineering (BioMed), 49–54, 2017, [http://doi.org/10.2316/P.2017.852-053]
DOI: https://doi.org/10.2316/P.2017.852-053
Sherif F., Mohamed W. A., Mohra A. S.: Skin lesion analysis toward melanoma detection using deep learning techniques. INTL Journal of Electronics and Telecommunications 65(4), 2019, 597–602.
Villa-Pulgarin J., Ruales-Torres A., Arias-Garzón D., Bravo-Ortiz M., Arteaga-Arteaga H., Mora-Rubio A., Alzate-Grisales J., Mercado-Ruiz E., Hassaballah M., Orozco-Arias S., Cardona-Morales O., Tabares-Soto R.: Optimized Convolutional Neural Network Models for Skin Lesion Classification. Computers, Materials & Continua Tech Science Press, CMC 70(2), 2022, [http://doi.org/10.32604/cmc.2022.019529].
DOI: https://doi.org/10.32604/cmc.2022.019529
Wang Y., Cai J., Louie D., Wang J., Lee T.: Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Computers in Biology and Medicine 137, 2021, 104812.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104812
Young A. T., Xiong M., Pfau J., Keiser M. J, Wei M.L.: Artificial intelligence in dermatology: A Primer. Journal of Investigative Dermatology 140, 2020, 1504–1512.
DOI: https://doi.org/10.1016/j.jid.2020.02.026
Yu L., Chen H., Dou Q., Qin J., Heng P. A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 2017, 994–1004.
DOI: https://doi.org/10.1109/TMI.2016.2642839
Zhang J., Xie Y., Wu Q., Xia Y.: Skin lesion classification in dermoscopy images using synergic deep learning, Springer Nature Switzerland. LNCS 11071, 2018, 12–20.
DOI: https://doi.org/10.1007/978-3-030-00934-2_2