MULTICLASS SKIN LESS IONS CLASSIFICATION BASED ON DEEP NEURAL NETWORKS

Magdalena Michalska

mmagamichalska@gmail.com
Lublin University of Technology, Department of Electronics and Information Technology (Poland)
http://orcid.org/0000-0002-0874-3285

Abstract

Skin diseases diagnosed with dermatoscopy are becoming more and more common. The use of computerized diagnostic systems becomes extremely effective. Non-invasive methods of diagnostics, such as deep neural networks, are an increasingly common tool studied by scientists. The article presents an overview of selected main issues related to the multi-class classification process: the stage of database selection, initial image processing, selection of the learning data set, classification tools, network training stage and obtaining final results. The described actions were implemented using available deep neural networks. The article pay attention to the final results of available models, such as effectiveness, specificity, classification accuracy for different numbers of classes and available data sets.


Keywords:

dermatoscopic images, multiclass classification, skin lesions, deep neural networks

Aburaed N., Panthakkan A., Al-Saad M., Amin S. A., Mansoor W.: Deep convolutional neural network (DCNN) for skin cancer classification. Proceedings of the 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2020, 1–4.
DOI: https://doi.org/10.1109/ICECS49266.2020.9294814   Google Scholar

Adegun A., Viriri S.: Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state of the art. Artif Intell Rev 54, 2021, 811–841.
DOI: https://doi.org/10.1007/s10462-020-09865-y   Google Scholar

Al-masni M. A., Kim D., Kim T.: Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer methods and programs in biomedicine 190, 2020, 105351.
DOI: https://doi.org/10.1016/j.cmpb.2020.105351   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

Cassidy B., Kendrick C., Brodzicki A., Jaworek-Korjakowska J., Yap M.: Analysis of the ISIC image datasets: usage, benchmarks and recommendations. Medical Image Analysis 75, 2022, 102305 [http://doi.org/10.1016/j.media.2021.102305].
DOI: https://doi.org/10.1016/j.media.2021.102305   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 [http://doi.org/10.1007/978-981-15-3383-9_15].
DOI: https://doi.org/10.1007/978-981-15-3383-9_15   Google Scholar

Codella N. C. F., Nguyen B., Pankanti S., Gutman D., Helba B., Halpern A., Smith J. R.: 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 (04.01.2021).
  Google Scholar

Ge Y., Li B., Zhao Y., Guan E., Yan W.: Melanoma segmentation 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, 2017, 250–258.
DOI: https://doi.org/10.1007/978-3-319-66179-7_29   Google Scholar

Gessert N., Sentker T., Madesta F. et al.: Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. 67, 2019, 495–503 [http://doi.org/10.1109/TBME.2019.2915839].
DOI: https://doi.org/10.1109/TBME.2019.2915839   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

Haenssle H. A., Fink C., Toberer F. 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, 2020, 137–143.
DOI: https://doi.org/10.1016/j.annonc.2019.10.013   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. [http://doi.org/10.1101/2021.02.02.21251038].
DOI: https://doi.org/10.1101/2021.02.02.21251038   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

Howard A. G., Zhu M., Chen B., Kalenichenko D., Wang W., Weyand T., Andreetto M. Hartwig A.: MobileNets: Efficient convolutional neural networks for mobile vision applications. Computer Science, Computer Vision and Pattern Recognitionar, Xiv:1704.04861v1 [http://doi.org/10.48550/arXiv.1704.04861].
  Google Scholar

Huang G., Liu Z., Maaten L., Weinberger K.: Densely Connected Convolutional Networks. Computer Vision and Pattern Recognition arXiv:1608.06993v5. [http://doi.org/10.48550/arXiv.1608.06993].
  Google Scholar

Iqbal I., Younus M., Walayat K., Ullah Kakar M., Ma J.: Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Computerized Medical Imaging and Graphics 88, 2021, 101843 [http://doi.org/10.1016/j.compmedimag.2020.101843].
DOI: https://doi.org/10.1016/j.compmedimag.2020.101843   Google Scholar

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

Kareem O. S., Abdulazee A. M., Zeebaree D. Q.: Skin lesions classification using deep learning techniques: Review. Asian Journal of Research in Computer Science 9(1), 2021, AJRCOS.68652, 1–22.
DOI: https://doi.org/10.9734/ajrcos/2021/v9i130210   Google Scholar

Lopez A. R., Giro-i-Nieto X., Burdick J., Marques O.: Skin lesion classification from dermatoscopic images using deep learning techniques. Conference Paper 2017 [http://doi.org/JO.23J6/P.20l7.852-053].
  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), 2009, 721–733.
DOI: https://doi.org/10.1109/TITB.2009.2017529   Google Scholar

Mahbod A., Schaefe G., Ellinger, I., Ecker R., Pitiot A., Wang C.: Fusing fine tuned deep features for skin lesion classification. Comput. Med. Imaging Graph. 71, 2019, 19–29 [http://doi.org/10.1016/j.compmedimag.2018.10.007].
DOI: https://doi.org/10.1016/j.compmedimag.2018.10.007   Google Scholar

Majumder S., Ahsan Ullah M.: Feature extraction from dermoscopy images for an effective diagnosis of melanoma skin cancer. 10th International Conference on Electrical and Computer Engineering Bangladesh, 2018, 185–188.
DOI: https://doi.org/10.1109/ICECE.2018.8636712   Google Scholar

Marchetti M. A., Liopyris K., Dusza S. W. 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, 2020, 622–627.
  Google Scholar

Marchetti M. A., Codella N. C., Dusza S. W. et al.: 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

MED-NODE Dataset http://www.cs.rug.nl/~imaging/databases/melanoma_naevi/ (23.03.2022).
  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

PAD-UFES-20 Dataset https://data.mendeley.com/datasets/zr7vgbcyr2/1 (23.03.2022)
  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, 2021, 012032 [http://doi.org/10.1088/1742-6596/1911/1/012032].
DOI: https://doi.org/10.1088/1742-6596/1911/1/012032   Google Scholar

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

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

Raza R., Zulfiqar F., Tariq S., Anwar G. B., Sargano A. B., Habib Z.: Melanoma classification from dermoscopy images using ensemble of convolutional neural networks. Mathematics 10(1), 2022, 26.
DOI: https://doi.org/10.3390/math10010026   Google Scholar

Rebouças Filho P. P., Peixoto S. A., Medeiros da Nobrega R. V., Hemanth D. J., Medeiros A. G., Sangaiah A. K., de Albuquerque V. H. C.: Automatic histologically-closer classification of skin lesions. Comput. Med. Imaging Graph. 68, 2018, 40–54 [http://doi.org/10.1016/j.compmedimag.2018.05.004].
DOI: https://doi.org/10.1016/j.compmedimag.2018.05.004   Google Scholar

Saeed J., Zeebaree S.: Skin lesion classification based on deep convolutional neural networks architectures. JASTT 2(01), 2021, 41–51.
DOI: https://doi.org/10.38094/jastt20189   Google Scholar

Salian A. C., Vaze S., Singh P., Shaikh G. N., Chapaneri S., Dayaswal D.: Skin lesion classification using deep learning architectures. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA) IEEE, 2020, 168–173.
DOI: https://doi.org/10.1109/CSCITA47329.2020.9137810   Google Scholar

Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L. C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, 4510–4520.
DOI: https://doi.org/10.1109/CVPR.2018.00474   Google Scholar

Srinivasu P. N., SivaSai J. G., Ijaz M. F., Bhoi A. K., Kim W., Kang J. J: Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21, 2852, 2021.
DOI: https://doi.org/10.3390/s21082852   Google Scholar

Tschandl P., Codella N., Akay B. N. 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), 2019, 938-947.
DOI: https://doi.org/10.1016/S1470-2045(19)30333-X   Google Scholar

Villa-Pulgarin J., Ruales-Torres A., Arias-Garzón D. et al.: Optimized Convolutional Neural Network Models for Skin Lesion Classification. Computers, Materials & Continua Tech Science Press, CMC 70(2), 2022, 2131–2148.
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

Wei L., Ding K., Hu H.: Automatic Skin Cancer Detection in Dermoscopy Images based on Ensemble Lightweight Deep Learning Network. IEEE Access 8, 2020, 99633–99647.
DOI: https://doi.org/10.1109/ACCESS.2020.2997710   Google Scholar

Xiao F., Wu Q. Visual saliency based global–local feature representation for skin cancer classification. IET Image Processing 14(10), 2020, 2140–2148.
DOI: https://doi.org/10.1049/iet-ipr.2019.1018   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

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

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Published
2022-06-30

Cited by

Michalska, M. (2022). MULTICLASS SKIN LESS IONS CLASSIFICATION BASED ON DEEP NEURAL NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(2), 10–14. https://doi.org/10.35784/iapgos.2963

Authors

Magdalena Michalska 
mmagamichalska@gmail.com
Lublin University of Technology, Department of Electronics and Information Technology Poland
http://orcid.org/0000-0002-0874-3285

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