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Combination of Deep Learning and Ambiguity Rejection for Improving Image-Based Disease Diagnosis

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Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13995))

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Abstract

Artificial intelligent (AI) based medical image recognition plays important task to assist in many disease diagnosis systems. In medical diagnosis, the incorrect decision is very serious. The healthcare diagnosis guides the treatment plan, and it is significant impact on the patient’s health outcomes. An incorrect diagnosis can lead to delays in treatment or even the wrong treatment being administered, which results in serious harm to the patient. In this article, we propose an approach to reject ambiguity samples in the classification results, which improve the accuracy of the medical image- based diseases diagnose. In this study, we also experimented using some well-known deep learning models such as MobileNet (lightweight architecture) and DenseNet (more complex and dense connected architecture). Additionally, we combine with some solutions to address the problem of the data imbalance such as focal loss and data augmentation techniques. In the classification stage, there are still significant misclassification results. Therefore, we present the solution for ambiguity rejection of uncertain samples. Experimental results show that the accuracy increases significantly after removing uncertain samples. The high removal rate of uncertain samples also affects to the diagnosing quality. This approach eliminates uncertain samples, which utilizes for improving the diagnosing quality from results of deep learning classification around 10% recall and 70% coverage rate, respectively.

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References

  1. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  2. Howard, A.G., et al.: Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861 (2017)

  3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

    Google Scholar 

  4. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  5. Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019)

    Google Scholar 

  6. Li, L., Talwalkar, A.: Random Search and Reproducibility for Neural Architecture Search. arXiv preprint arXiv:1902.07638 (2019)

  7. Bertrand, H., Ardon, R., Perrot, M., Bloch, I.: Hyperparameter optimization of deep neural networks: combining hyperband with Bayesian model selection. Conférence sur l’Apprentissage Automatique (2017)

    Google Scholar 

  8. Hoang, V.-T., Huang, D.-S., Jo, K.-H.: 3-D facial landmarks detection for intelligent video systems. IEEE Trans. Industr. Inf. 17, 578–586 (2020)

    Article  Google Scholar 

  9. Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. J. Machine Learning Res. 18, 826–830 (2017)

    MathSciNet  Google Scholar 

  10. Tran, D.-P., Nguyen, G.-N., Hoang, V.-D.: Hyperparameter optimization for improving recognition efficiency of an adaptive learning system. IEEE Access 8, 160569–160580 (2020)

    Article  Google Scholar 

  11. Dikov, G., van der Smagt, P., Bayer, J.: Bayesian Learning of Neural Network Architectures. arXiv preprint arXiv:1901.04436 (2019)

  12. Huang, C., Lucey, S., Ramanan, D.: Learning policies for adaptive tracking with deep feature cascades. Proceedings of the IEEE International Conference on Computer Vision, pp. 105–114 (2017)

    Google Scholar 

  13. Long, M., Cao, Y., Cao, Z., Wang, J., Jordan, M.I.: Transferable representation learning with deep adaptation networks. IEEE Trans. Pattern Anal. Mach. Intell. 41, 3071–3085 (2018)

    Article  Google Scholar 

  14. Le, N.Q.K., Huynh, T.-T., Yapp, E.K.Y., Yeh, H.-Y.: Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Comput. Methods Programs Biomed. 177, 81–88 (2019)

    Article  Google Scholar 

  15. Snoek, J., et al.: Scalable bayesian optimization using deep neural networks. International Conference on Machine Learning, pp. 2171–2180 (2015)

    Google Scholar 

  16. Carli, P., et al.: Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Br. J. Dermatol. 148, 981–984 (2003)

    Article  Google Scholar 

  17. Qian, C., et al.: A Two-Stage Method for Skin Lesion Analysis. arXiv preprint arXiv:1809.03917 (2018)

  18. Yi, H., Jiang, Q., Yan, X., Wang, B.: Imbalanced classification based on minority clustering synthetic minority oversampling technique with wind turbine fault detection application. IEEE Trans. Industr. Inf. 17, 5867–5875 (2020)

    Article  Google Scholar 

  19. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artificial Intell. Res. 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  20. Niu, Z., Zhong, G., Yu, H.: A review on the attention mechanism of deep learning. Neurocomputing 452, 48–62 (2021)

    Article  Google Scholar 

  21. Geifman, Y., El-Yaniv, R.: Selectivenet: A deep neural network with an integrated reject option. International Conference on Machine Learning, pp. 2151–2159 (2019)

    Google Scholar 

  22. Franc, V., Prusa, D., Voracek, V.: Optimal strategies for reject option classifiers. J. Mach. Learn. Res. 24, 1–49 (2023)

    MathSciNet  Google Scholar 

  23. Kashani Motlagh, N., Davis, J., Anderson, T., Gwinnup, J.: Learning when to say “i don’t know. Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I, pp. 196–210 (2022)

    Google Scholar 

  24. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2980–2988 (2017)

    Google Scholar 

  25. Codella, N., et al.: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (isic). arXiv preprint arXiv:1902.03368 (2019)

  26. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5, 1–9 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Ho Chi Minh University of Banking, Ho Chi Minh City, Vietnam

    Thanh-An Pham

  2. Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam

    Van-Dung Hoang

Authors
  1. Thanh-An Pham
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  2. Van-Dung Hoang
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Corresponding author

Correspondence to Van-Dung Hoang .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University Iwate, Iwate, Japan

    Hamido Fujita

  4. Wroclaw University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

Appendix

Appendix

Table 5. Detail of classified results on validation dataset1, validation dataset2. The average result is formed (result1 on dataset1 + result1 on dataset1)/2.
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Table 6. Detail of ambiguity rejection results on validation dataset1 and validation dataset2
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pham, TA., Hoang, VD. (2023). Combination of Deep Learning and Ambiguity Rejection for Improving Image-Based Disease Diagnosis. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_12

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  • DOI: https://doi.org/10.1007/978-981-99-5834-4_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5833-7

  • Online ISBN: 978-981-99-5834-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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