SIECI NEURONOWE Z KERAS W DIAGNOSTYCE ZMIAN SKÓRNYCH

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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.
  Google Scholar

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
  Google Scholar

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   Google Scholar

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.
  Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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.
  Google Scholar

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   Google Scholar

Hijazi S., Kumar R., Rowen C.: Using convolutional neural networks for image recognition. Cadence Design Systems Inc., San Jose 2015.
  Google Scholar

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   Google Scholar

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   Google Scholar

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].
  Google Scholar

Lopez A. R.: Skin lesion classification from dermoscopic images using deep learning techniques. Proc. 13th IASTED Int. Conf. Biomed. Eng. 2017, 49–54.
  Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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.
  Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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.
  Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar