GŁĘBOKIE SIECI NEURONOWE DLA DIAGNOSTYKI ZMIAN SKÓRNYCH

Abunadi I., Senan E. M.: Deep learning and machine learning techniques of diagnosis dermoscopy images for early detection of skin diseases. Electronics 10, 3158, 2021.
DOI: https://doi.org/10.3390/electronics10243158   Google Scholar

Al-Masni M. A., Kim D. H., Kim T. S:. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed. 190, 105351, 2020.
DOI: https://doi.org/10.1016/j.cmpb.2020.105351   Google Scholar

Brinker T. J. et al: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113, 47–54, 2019.
  Google Scholar

Chaturvedi S. S., Gupta K., Prasad P. S.: Skin lesion analyser: An efficient seven-way multi-class skin cancer classification using MobileNet. Advances in Intelligent Systems and Computing 1141, Springer, Singapore, 2020.
DOI: https://doi.org/10.1007/978-981-15-3383-9_15   Google Scholar

Codella N. C. F. et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development 61(4/5), 173, 2017.
DOI: https://doi.org/10.1147/JRD.2017.2708299   Google Scholar

DERMOFIT IMAGE LIBRARY [https://licensing.edinburghinnovations.ed.ac.uk/i/software/dermofit-imagelibrary.html?item=dermofit-image-library] (accessed 04.01.2021).
  Google Scholar

Gavrilov D., Lazarenko L., Zakirov E.: AI recognition in skin pathologies detection. Proceedings of the 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI), 554–542, Belgrade 2019.
DOI: https://doi.org/10.1109/IC-AIAI48757.2019.00017   Google Scholar

Ge Y. et al.: Melanoma segmentation and classification in clinical images using deep learning. 10th International Conference on Machine Learning and Computing ICMLC, 2018, 252–256.
DOI: https://doi.org/10.1145/3195106.3195164   Google Scholar

Gessert N. et al.: Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. 67, 495–503, 2020.
DOI: https://doi.org/10.1109/TBME.2019.2915839   Google Scholar

Haenssle H. A. et al: Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermato-logists working under less artificial conditions. Ann Oncol 31, 137–143, 2020.
  Google Scholar

Harangi B.: Skin lesion classification with ensembles of deep convolutional neural networks. J. Biomed. Inform. 86, 25–32, 2018.
DOI: https://doi.org/10.1016/j.jbi.2018.08.006   Google Scholar

Hasan M. M., Elahi M., Alam M. A.: DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning and augmentation. medRxiv 2021.02.02.21251038.
DOI: https://doi.org/10.1101/2021.02.02.21251038   Google Scholar

He K. et al.: Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, 770–778.
DOI: https://doi.org/10.1109/CVPR.2016.90   Google Scholar

Hekler A. et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images, Eur J Cancer 118, 91–96, 2019.
DOI: https://doi.org/10.1016/j.ejca.2019.06.012   Google Scholar

Howard A. G. et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications. Computer Science, Computer Vision and Pattern Recognition, arXiv:1704.04861v1.
  Google Scholar

Huang G. et al.: Densely Connected Convolutional Networks. Computer Vision and Pattern Recognition arXiv:1608.06993v5.
  Google Scholar

Iqbal I. et al.: Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Computerized Medical Imaging and Graphics 88, 101843, 2021.
DOI: https://doi.org/10.1016/j.compmedimag.2020.101843   Google Scholar

ISIC Archive [https://www.isic-archive.com/#!/topWithHeader/onlyHeaderTop/gallery] (accessed 23.03.2022).
  Google Scholar

Kareem O., Mohsin Abdulazeez A., Zeebaree D.: Skin Lesions Classification Using Deep Learning Techniques: Review. Asian Journal of Research in Computer Science 9(1), 1–22, 2021.
DOI: https://doi.org/10.9734/ajrcos/2021/v9i130210   Google Scholar

Lee S. et al.: Augmented decision-making for acrallentiginous melanoma detection using deep convolutional neural networks. J. Eur. Acad. Dermatol. Venereol. 34, 1842–1850, 2020.
DOI: https://doi.org/10.1111/jdv.16185   Google Scholar

Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques. 13th International Conference on Biomedical Engineering (BioMed) IASTED, 2017, 49–54.
  Google Scholar

Maglogiannis I., Doukas C. N.: Overview of advanced computer vision systems for skin lesions characterization, IEEE transactions on information technology in biomedicine 13(5), 721–733, 2009.
DOI: https://doi.org/10.1109/TITB.2009.2017529   Google Scholar

Mahbod A. et al.: Fusing finetuned deep features for skin lesion classification, Comput. Med. Imaging Graph. 71, 19–29, 2019.
DOI: https://doi.org/10.1016/j.compmedimag.2018.10.007   Google Scholar

Mahdianpari M. et al.: Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens. 10(7), 2018.
DOI: https://doi.org/10.3390/rs10071119   Google Scholar

Marchetti M. A. et al.: 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, 622–627, 2020.
DOI: https://doi.org/10.1016/j.jaad.2019.07.016   Google Scholar

Maron R. C. et al.: Systematic outperformance of 112 dermato-logists in multiclass skin cancer image classification by convo-lutional neural networks, Eur J Cancer 119, 57–65, 2019.
  Google Scholar

MED-NODE Dataset [http://www.cs.rug.nl/~imaging/databases/melanoma_naevi/] (accessed 23.03.2022).
DOI: https://doi.org/10.48189/nl.2022.v03i2.004   Google Scholar

Nida N. et al.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering, International Journal of Medical Informatics 124, 37–48, 2019.
DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.005   Google Scholar

PAD-UFES-20 Dataset [https://data.mendeley.com/datasets/zr7vgbcyr2/1] (accessed: 23.03.2022).
  Google Scholar

PH2 Dataset [https://www.fc.up.pt/addi/ph2%20database.html] (accessed 23.03.2022).
  Google Scholar

Qin Z. et al.: A GAN-based image synthesis method for skin lesion classification. Computer Methods and Programs in Biomedicine, 105568, 2020.
DOI: https://doi.org/10.1016/j.cmpb.2020.105568   Google Scholar

Raza R. et al.: Melanoma Classification from dermoscopy images using ensemble of convolutional neural networks. Mathematics 10, 26, 2022.
DOI: https://doi.org/10.3390/math10010026   Google Scholar

Sandler M. et al.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, 4510–4520.
DOI: https://doi.org/10.1109/CVPR.2018.00474   Google Scholar

Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations ICLR, 2015.
  Google Scholar

Szegedy C. et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI, 4278–4284, 2017.
DOI: https://doi.org/10.1609/aaai.v31i1.11231   Google Scholar

Tschandl P. et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, webbased, international, diagnostic study. Lancet Oncol 2019b(20), 938–947, 2019.
DOI: https://doi.org/10.1016/S1470-2045(19)30333-X   Google Scholar

Villa-Pulgarin J. et al.: Optimized convolutional neural network models for skin lesion classification, Computers, Materials & Continua Tech Science Press, CMC 70(2), 2022
DOI: https://doi.org/10.32604/cmc.2022.019529   Google Scholar

Yu C. et al.: Acral melanoma detection using a convolutional neural network for dermoscopy images. PLoS ONE 2018, 13, e0193321, 2018.
DOI: https://doi.org/10.1371/journal.pone.0193321   Google Scholar

Zakład Epidemiologii i Prewencji Nowotworów Centrum Onkologii – Instytut w Warszawie. Krajowy Rejestr Nowotworów (KRN) [http://onkologia.org.pl/] (accessed 02.08.2019).
  Google Scholar