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Big dermatological data service for precise and immediate diagnosis by utilizing pre-trained learning models

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Abstract

Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures. Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer.

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References

  1. Aqel, D., Al-Zubi, S., Mughaid, A., Jararweh, Y.: Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Clust. Comput. 25(3), 2007–2020 (2022)

    Article  Google Scholar 

  2. Jararweh, Y., Al-Ayyoub, M., Fakirah, M., Alawneh, L., Gupta, B.B.: Improving the performance of the Needleman–Wunsch algorithm using parallelization and vectorization techniques. Multimedia Tools Appl. 78, 3961–3977 (2019)

    Article  Google Scholar 

  3. Esteva, A.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Masood, A., Al-Jumaily, A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging (2013). https://doi.org/10.1155/2013/323268

    Article  PubMed  PubMed Central  Google Scholar 

  5. Thapar, P., Rakhra, M., Cazzato, G., Hossain, M.S., et al.: A novel hybrid deep learning approach for skin lesion segmentation and classification. J. Healthc. Eng. (2022). https://doi.org/10.1155/2022/1709842

    Article  PubMed  PubMed Central  Google Scholar 

  6. Thapar, P., Rakhra, M., Singh, A.: The epidemiology of automatic skin cancer detection by comparative analysis of pre-processing and segmentation techniques. In: 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE, pp. 894–899 (2022)

  7. Fan, X.: Effect of image noise on the classification of skin lesions using deep convolutional neural networks. http://creativecommons.org/licenses/by/4.0/

  8. Bakkouri, I., Afdel, K.: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimedia Tools Appl. 79(29–30), 20483–20518 (2020). https://doi.org/10.1007/s11042-019-07988-1

    Article  Google Scholar 

  9. Lucieri, A., Bajwa, M., Braun, Malik, M., Dengel, A., Ahmed, S.: Exaid: a multimodal explanation framework for computer-aided diagnosis of skin lesions. arXiV Preprint. arxiv:2201.01249

  10. Allugunti, V.: A machine learning model for skin disease classification using convolution neural network healthcare view project a machine learning model for skin disease classification using convolution neural network. Int. J. Comput. Program. Database Manag. 3(1), 141–147 (2022)

    Article  Google Scholar 

  11. Cai, G., Zhu, Y., Wu, Y., Jiang, X., Ye, J., Yang, D.: A multimodal transformer to fuse images and metadata for skin disease classification. Vis Comput. (2022). https://doi.org/10.1007/s00371-022-02492-4

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hwang, S., Shin, H., Park, J., Kwon, B., Kang, M.: Classification of dog skin diseases using deep learning with images captured from multispectral imaging device. Mol. Cell. Toxicol. 18(3), 299–309 (2022). https://doi.org/10.1007/s13273-022-00249-7

    Article  CAS  Google Scholar 

  13. Polat, K., Koc, K.: Detection of skin diseases from dermoscopy image using the combination of convolutional neural network and one-versus-all. J. Artif. Intell. Syst. 2(1), 80–97 (2020). https://doi.org/10.33969/AIS.2020.21006

    Article  Google Scholar 

  14. Ahmad, B., Usama, M., Huang, C., Hwang, K., Hossain, M., Muhammad, G.: Discriminative feature learning for skin disease classification using deep convolutional neural network. IEEE Access 8, 39025–39033 (2020). https://doi.org/10.1109/ACCESS.2020.2975198

    Article  Google Scholar 

  15. Kinyanjui, N.: Estimating skin tone and effects on classification performance in dermatology datasets. arXiV Preprint. arxiv:1910.13268

  16. Roslan, R., Razly, I., Sabri, N., Ibrahim, Z.: Evaluation of psoriasis skin disease classification using convolutional neural network. IAES Int. J. Artif. Intell. 9(2), 349–355 (2020). https://doi.org/10.11591/ijai.v9.i2.pp349-355

    Article  Google Scholar 

  17. Gu, S., Pednekar, M., Slater, R.: Improve image classification using data augmentation and neural networks. https://scholar.smu.edu/datasciencereview/vol2/iss2/1

  18. Wu, Z.: Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access 7, 66505–66511 (2019). https://doi.org/10.1109/ACCESS.2019.2918221

    Article  Google Scholar 

  19. Schuelke, S., Aurit, S., Connot, N., Denney, S.: Virtual nursing: the new reality in quality care. Nurs. Adm. Q. 43(4), 322–328 (2019). https://doi.org/10.1097/NAQ.0000000000000376

    Article  PubMed  Google Scholar 

  20. Wang, S.: Artificial intelligence in lung cancer pathology image analysis. Cancers 11(11), 1673 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ahsan, M.M., Uddin, M.R., Luna, S.A.: Monkey pox image data collection. arXiv preprint (2022). arXiv:2206.01774

  22. Majumder, P.: Analyses of polynomial neural networks for prediction of the prevalence of Monkeypox infections in Asia and around the world. Electron. J. Gen. Med. 19(6), 410 (2022). https://doi.org/10.29333/ejgm/12400

    Article  Google Scholar 

  23. Sitaula, C., Shahi, T.B.: Monkeypox virus detection using pre-trained deep learning-based approaches. J. Med. Syst. 46, 78 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  24. Joshua, J., Nlerum, P.: A neuro-fussy based model for diagnosis of Monkeypox diseases a neuro-fussy based model for diagnosis of Monkeypox diseases. Int. J. Comput. Sci. Trends Technol. (IJCST) 6(2), 143–153 (2018)

    Google Scholar 

  25. Ali, S.: Monkeypox skin lesion detection using deep learning models: a feasibility study. arXiV Preprint (2022). arxiv:2207.03342

  26. AlZu’bi, S., Jararweh, Y., Al-Zoubi, H., Elbes, M., Kanan, T., Gupta, B.: Multi-orientation geometric medical volumes segmentation using 3d multiresolution analysis. Multimedia Tools Appl. 78(17), 24223–24248 (2019)

    Article  Google Scholar 

  27. Al-Zu’bi, S., Hawashin, B., Mughaid, A., Baker, T.: Efficient 3d medical image segmentation algorithm over a secured multimedia network. Multimedia Tools Appl. 80(11), 16887–16905 (2021)

    Article  Google Scholar 

  28. Costache, A., Popescu, D.: Emotion sketches facial expression recognition in diversity groups. U.P.B. Sci. Bull. Ser. C 83(4), 1–12 (2021)

    Google Scholar 

  29. Kolides, A., Nawaz, A., Rathor, A., Beeman, D., Hashmi, M., Fatima, S., Berdik, D., Al-Ayyoub, M., Jararweh, Y.: Artificial intelligence foundation and pre-trained models: fundamentals, applications, opportunities, and social impacts. Simul. Model. Pract. Theory 126, 102754 (2023)

    Article  Google Scholar 

  30. Wen, D., Khan, S.M., Xu, A.J., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L.: Characteristics of publicly available skin cancer image datasets: a systematic review. Lancet Digit. Health 10, 712 (2023)

    Google Scholar 

  31. Sitaula, C., Shahi, T.B.: Monkeypox virus detection using pre-trained deep learning-based approaches. J. Med. Syst. 46(11), 1–9 (2022)

    Article  Google Scholar 

  32. Alzoubi, O., Awad, M.A., Abdalla, A.M.: Automatic segmentation and detection system for varicocele in supine position. IEEE Access 9, 125393–125402 (2021)

    Article  Google Scholar 

  33. Thapar, P., Rakhra, M., Singh, A.: Comparing image feature extraction methods using dermoscopy noisy images. In: IEEE International Mobile and Embedded Technology Conference (MECON), vol. 2022, pp. 559–562 (2022)

  34. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Sciences and IT, Al Zaytoonah University of Jordan, P.O. Box 130, Amman, 11733, Jordan

    Mohammed Elbes, Shadi AlZu’bi, Tarek Kanan & Samia Abushanab

  2. Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah for IT, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Ala Mughaid

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  1. Mohammed Elbes
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  4. Ala Mughaid
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  5. Samia Abushanab
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With the submission of this manuscript I would like to undertake that: All authors of this article have directly participated in the planning, execution, or analysis of this study. All authors of this paper have read and approved the final version submitted. The contents of this manuscript have not been copyrighted or published previously. The contents of this manuscript are not now under consideration for publication elsewhere.

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Elbes, M., AlZu’bi, S., Kanan, T. et al. Big dermatological data service for precise and immediate diagnosis by utilizing pre-trained learning models. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04331-8

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