GŁĘBOKIE SIECI NEURONOWE DLA DIAGNOSTYKI ZMIAN SKÓRNYCH

Magdalena Michalska-Ciekańska

doi:10.35784/iapgos.3042

Open full text

PDF

Published: Sep 30, 2022

DOI: https://doi.org/10.35784/iapgos.3042

Issue Vol. 12 No. 3 (2022)

Articles

ABSORPTION CHARACTERISTICS OF THERMAL RADIATION FOR CARBON DIOXIDE
Jan Kubicki, Krzysztof Kopczyński, Jarosław Młyńczak

4-7
SWITCH-FILTER ON A RECTANGULAR WAVEGUIDE PARTIALLY FILLED BY DIELECTRIC
Vitaly Pochernyaev, Nataliia Syvkova, Mariia Mahomedova

8-11
GIANT MAGNETORESISTANCE OBSERVED IN THIN FILM NiFe/Cu/NiFe STRUCTURES
Jakub Kisała, Andrzej Kociubiński, Karolina Czarnacka, Mateusz Gęca

12-15
EXPANSION OF THE ZONE OF PRACTICAL APPLICATION OF PLC WITH PARALLEL ARCHITECTURE
Sergiy Tymchuk, Oleksiy Piskarev, Oleksandr Miroshnyk, Serhii Halko, Taras Shchur

16-19
FEATURES OF THE ANGULAR SPEED DYNAMIC MEASUREMENTS WITH THE USE OF AN ENCODER
Vasyl Kukharchuk, Waldemar Wójcik, Sergii Pavlov, Samoil Katsyv, Volodymyr Holodiuk, Oleksandr Reyda, Ainur Kozbakova, Gaukhar Borankulova

20-26
APPLICATION OF PREDICTIVE MAINTENANCE IN THE PACKAGING PRODUCTION
Bogdan Palchevskyi, Lyubov Krestyanpol

27-33
PREDICTION MODEL OF PUBLIC HOUSES’ HEATING SYSTEMS: A COMPARISON OF SUPPORT VECTOR MACHINE METHOD AND RANDOM FOREST METHOD
Andrii Perekrest, Vladimir Chenchevoi, Olga Chencheva, Alexandr Kovalenko, Mykhailo Kushch-Zhyrko, Aliya Kalizhanova, Yedilkhan Amirgaliyev

34-39
NATURAL-SIMULATION MODEL OF PHOTOVOLTAIC STATION GENERATION IN PROCESS OF ELECTRICITY BALANCING IN ELECTRICAL POWER SYSTEM
Petr Lezhniuk, Viacheslav Komar, Iryna Hunko, Daniyar Jarykbassov, Dinara Tussupzhanova, Bakhyt Yeraliyeva, Nazbek Katayev

40-45
APPLICATION FOR VIBRATION DIAGNOSTICS
Anzhelika Stakhova

46-49
DEEP NEURAL NETWORKS FOR SKIN LESIONS DIAGNOSTICS
Magdalena Michalska-Ciekańska

50-53
DYNAMIC AND MATHEMATICAL MODELS OF THE HYDROIMPULSIVE VIBRO-CUTTING DEVICE WITH A PRESSURE PULSE GENERATOR BULT INTO THE RING SPRING
Roman Obertyukh, Andrіі Slabkyі, Leonid Polishchuk, Oleksandr Povstianoi, Saule Kumargazhanova, Maxatbek Satymbekov

54-58
EXPERT FUZZY SYSTEMS FOR EVALUATION OF INTENSITY OF REACTIVE EDEMA OF SOFT TISSUES IN PATIENTS WITH DIABETES
Liudmyla Shkilniak, Waldemar Wójcik, Sergii Pavlov, Oleg Vlasenko, Tetiana Kanishyna, Irina Khomyuk, Oleh Bezverkhyi, Sofia Dembitska, Orken Mamyrbayev, Aigul Iskakova

59-63
SIMULATION OF INFORMATION SECURITY RISKS OF AVAILABILITY OF PROJECT DOCUMENTS BASED ON FUZZY LOGIC
Oleksii M. Shushura, Liudmyla A. Asieieva, Oleksiy L. Nedashkivskiy, Yevhen V. Havrylko, Yevheniia O. Moroz, Saule S. Smailova, Magzhan Sarsembayev

64-68
MANAGEMENT OF THE WORKPLACES BY THE FACILITIES OF OPERATIONS RESEARCH
Nataliia Geseleva, Ganna Proniuk, Olexander Romanyuk, Olga Akimova, Tetiana Troianovska-Korobeynikova, Liudmyla Savytska, Saule Rakhmetullina, Nurbapa Mekebayev

69-73
MODELING OF LABOR POTENTIAL OF UKRAINE: FORMATION OF KNOWLEDGE BASE
Ivan V. Zayukov, Iryna M. Kobylianska, Alexandr Kobylianskyi, Sofia V. Dembitska

74-78

DOI

https://doi.org/10.35784/iapgos.3042

Authors

Magdalena Michalska-Ciekańska

mmagamichalska@gmail.com

Lublin University of Technology, Poland

http://orcid.org/0000-0002-0874-3285

Abstract

Non-invasive diagnosis of skin cancer is extremely necessary. In recent years, deep neural networks and transfer learning have been very popular in the diagnosis of skin diseases. The article contains selected basics of deep neural networks, their interesting applications created in recent years, allowing the classification of skin lesions from available dermatoscopic images.

Keywords:

deep neural networks, transfer learning, dermatoscopic images, skin lesions diagnostics

References

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

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

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.

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Michalska-Ciekańska, M. (2022). DEEP NEURAL NETWORKS FOR SKIN LESIONS DIAGNOSTICS . Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(3), 50–53. https://doi.org/10.35784/iapgos.3042

DEEP NEURAL NETWORKS FOR SKIN LESIONS DIAGNOSTICS

Issue Vol. 12 No. 3 (2022)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 12 No. 3 (2022)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License