Skip to main content
SpringerLink
Log in

SkinNet: Enhancing Dermatological Diagnosis Through a New Deep Learning Framework

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023) (AI2SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 904))

Abstract

Skin cancer is a prevalent and potentially deadly disease, with early detection and treatment crucial for improved outcomes. With technological advances, the Internet of Things (IoT) in telemedicine has become increasingly popular, enabling remote diagnosis and treatment of diseases. In this paper, we investigate the feasibility of using IoT for skin cancer prediction and diagnosis in telemedicine. We propose a system that uses a combination of IoT devices, deep learning algorithms, and medical imaging techniques to predict the presence of skin cancer. The system comprises a smartphone with a high-resolution camera, a dermoscopy, and a cloud-based machine learning model. The smartphone camera captures images of the skin lesions, while the dermoscopy provides magnified images of the lesion’s surface for further analysis. The algorithm analyzes various features of the lesion, such as color, texture, and shape, to predict the likelihood of malignancy. The results are then transmitted to the cloud-based machine learning model, which further analyzes the data and provides a diagnosis. We evaluated the performance of our system using a dataset of skin cancer images, and our results show that the proposed system achieves a high accuracy rate of over 97,6% in predicting skin cancer. Additionally, the system provides a fast and efficient way to diagnose skin cancer, allowing for early detection and timely treatment, which can significantly improve patient outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adegun, A.A., Viriri, S.: FCN-based DenseNet framework for automated detection and classification of skin lesions in dermoscopy images 8, 150377–150396 https://doi.org/10.1109/ACCESS.2020.3016651. Conference Name: IEEE Access

  2. Kumar, M., Alshehri, M., AlGhamdi, R., Sharma, P., Deep, V.: A DE-ANN inspired skin cancer detection approach using fuzzy c-means clustering 25(4), 1319–1329 https://doi.org/10.1007/s11036-020-01550-2. Accessed May 5 2023

  3. Chaturvedi, S.S., Tembhurne, J.V., Diwan, T.: A multi-class skin Cancer classification using deep convolutional neural networks. Multimedia Tools Appl. 79(39), 28477–28498 (2020). https://doi.org/10.1007/s11042-020-09388-2

    Article  Google Scholar 

  4. Khan, M.A., Akram, T., Zhang, Y.-D., Sharif, M.: Attributes based skin lesion detection and recognition: a mask RCNN and transfer learning-based deep learning framework. Patt. Recogn. Lett.143, 58–66 https://doi.org/10.1016/j.patrec.2020.12.015 Accessed 5 May 2023

  5. 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(8), 2852 https://doi.org/10.3390/s21082852. Number: 8 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 5 May 2023

  6. Hosny, K.M., Kassem, M.A., Fouad, M.M.: Classification of skin lesions into seven classes using transfer learning with AlexNet. J. Digital Imag. 33(5), 1325–1334 (2020). https://doi.org/10.1007/s10278-020-00371-9

    Article  Google Scholar 

  7. Sae-Lim, W., Wettayaprasit, W., Aiyarak, P.: Convolutional neural networks using MobileNet for skin lesion classification. In: 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 242– 247. https://doi.org/10.1109/JCSSE.2019.8864155. ISSN: 2642-6579

  8. Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y.-T., Wang, X.-L., Badami, B.: Skin cancer diagnosis based on optimized convolutional neural network. Artif. Intell. Med. 102, 101756 https://doi.org/10.1016/j.artmed.2019.101756 Accessed 5 May 2023

  9. Hasan, M.K., Dahal, L., Samarakoon, P.N., Tushar, F.I., Martí, R.: DSNet: Automatic dermoscopic skin lesion segmentation. Comput. Biol. Med. 120, 103738 https://doi.org/10.1016/j.compbiomed.2020.103738 Accessed 5 May 2023

  10. Chatterjee, S., Dey, D., Munshi, S.: Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification. Comput. Methods Progr. Biomed. 178, 201–218 https://doi.org/10.1016/j.cmpb.2019.06.018 Accessed 5 May 2023

  11. Moradi, N., Mahdavi-Amiri, N.: Kernel sparse representation based model for skin lesions segmentation and classification. Comput. Methods Progr. Biomed. 182, 105038 https://doi.org/10.1016/j.cmpb.2019.105038. Accessed 5 May 2023

  12. Sáez, A., Sánchez-Monedero, J., Gutiérrez, P.A., Hervás-Martínez, C.: Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images 35(4), 1036–1045 https://doi.org/10.1109/TMI.2015.2506270. Conference Name: IEEE Transactions on Medical Imaging

  13. Yu, Z., et al.: Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Trans. Biomed. Eng. 66(4), 1006–1016 https://doi.org/10.1109/TBME.2018.2866166

  14. Hameed, N., Shabut, A.M., Ghosh, M.K., Hossain, M.A.: Multi-class multilevel classification algorithm for skin lesions classification using machine learning techniques. Expert Syst. Appl. 141, 112961 https://doi.org/10.1016/j.eswa.2019.112961 Accessed May 5 2023

  15. Hoang, L., Lee, S.-H., Lee, E.-J., Kwon, K.-R.: Multiclass skin lesion classification using a novel lightweight deep learning framework for smart healthcare 12(5), 2677 https://doi.org/10.3390/app12052677. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 5 May 2023

  16. Khan, M.A., Zhang, Y.-D., Sharif, M., Akram, T.: Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Comput. Electr. Eng. 90, 106956 https://doi.org/10.1016/j.compeleceng.2020.106956 Accessed 5 May 2023

  17. Thurnhofer-Hemsi, K., López-Rubio, E., Domínguez, E., Elizondo, D.A.: Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting. IEEE Access 9, 112193–112205 (2021). https://doi.org/10.1109/ACCESS.2021

  18. Pereira, P.M.M., et al.: Skin lesion classification enhancement using border-line features - the melanoma vs nevus problem 57, 101765 https://doi.org/10.1016/j.bspc.2019.101765. Accessed 05 May 2023

  19. Shetty, B., Fernandes, R., Rodrigues, A.P., Chengoden, R., Bhattacharya, S., Lakshmanna, K.: Skin lesion classification of dermoscopic images using machine learning and convolutional neural network 12(1), 18134 (2022) https://doi.org/10.1038/s41598-022-22644-9. Number: 1 Publisher: Nature Publishing Group. Accessed 2023-03-23

  20. Dhivyaa, C.R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., Johnpaul, P.: Skin lesion classification using decision trees and random forest algorithms. J. Ambient Intell. Human. Comput. 1–13 (2020). https://doi.org/10.1007/s12652-020-02675-8

  21. Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., Ellinger, I.: Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification . Comput. Methods Progr. Biomed. 193, 105475 https://doi.org/10.1016/j.cmpb.2020.105475 Accessed 5 May 2023

  22. Alenezi, F., Armghan, A., Polat, K.:Wavelet transform based deep residual neural network and ReLU based extreme learning machine for skin lesion classification. Expert Syst. Appl. 213, 119064 https://doi.org/10.1016/j.eswa.2022.119064 Accessed 29 Dec 2022

  23. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin. Sci. Data lesions 5(1), 180161 https://doi.org/10.1038/sdata.2018.161. Number: 1 Publisher: Nature Publishing Group. Accessed 5 May 2023

Download references

Author information

Authors and Affiliations

  1. Clinical and Medical Sciences and Biomedical Engineering Laboratory, Mohammed VI University of Sciences and Health, Casablanca, Morocco

    Aymane Edder, Fatima-Ezzahraa Ben-Bouazza & Bassma Jioudi

  2. LaMSN, La Maison des Sciences Numériques, Paris, France

    Fatima-Ezzahraa Ben-Bouazza

Authors
  1. Aymane Edder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatima-Ezzahraa Ben-Bouazza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bassma Jioudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aymane Edder .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  2. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Automatics and Applied Software at the Faculty of Engineering, University “Aurel Vlaicu” Arad and Romanian Academy of Scientists, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Edder, A., Ben-Bouazza, FE., Jioudi, B. (2024). SkinNet: Enhancing Dermatological Diagnosis Through a New Deep Learning Framework. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_17

Download citation

Publish with us

Policies and ethics

Search

Navigation