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Tuberculosis Disease Diagnosis Using Controlled Super Resolution

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Big Data and Artificial Intelligence (BDA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14418))

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Abstract

Tuberculosis (TB) is a chronic respiratory disease caused by a bacterial infection and has one of the highest mortality worldwide. Timely and precise TB detection is crucial as it can be dangerous if left untreated. To achieve accurate results, it is essential to have a high-resolution input. This paper introduces a Low and high level feature steering (LHFS) module, which reconstructs high-resolution images by a reference image that contains same information to the low-resolution input. Additionally, the Selective feature integration (SFI) module seamlessly integrates Ref image features into extracted features of LR image. The proposed model for factors 2, 4, and 6, attains super resolution metrics such as PSNR values of 30.225, 31.176, 33.836, and SSIM values of 0.8642, 0.8801, 0.9052 with classification metric accuracy values of 99.66, 98.96 and 98.32 respectively.

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References

  1. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  2. Chen, H., Gu, J., Zhang, Z.: Attention in attention network for image super-resolution. arXiv preprint arXiv:2104.09497

  3. Hu, Y., Li, J., Huang, Y., Gao, X.: Channel-wise and spatial feature modulation network for single image super-resolution. IEEE Trans. Circuits Syst. Video Technol. 30(11), 3911–3927 (2019)

    Article  Google Scholar 

  4. Dai, T., Zha, H., Jiang, Y., Xia, S.-T.: Image super-resolution via residual block attention networks, pp. 3879–3886 (2019). https://doi.org/10.1109/ICCVW.2019.00481

  5. Zheng, H., Ji, M., Wang, H., Liu, Y., Fang, L.: CrossNet: an end-to-end reference-based super resolution network using cross-scale warping. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 87–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_6

    Chapter  Google Scholar 

  6. Zhang, Z., Wang, Z., Lin, Z., Qi, H.: Image super-resolution by neural texture transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7982–7991 (2019)

    Google Scholar 

  7. Lu, L., Li, W., Tao, X., Lu, J., Jia, J.: MASA-SR: matching acceleration and spatial adaptation for reference-based image super-resolution. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6368–6377 (2021)

    Google Scholar 

  8. Yeswanth, P.V., Deivalakshmi, S., George, S., Ko, S.-B.: Residual skip network-based super-resolution for leaf disease detection of grape plant. Circ. Syst. Signal Process. 42(11), 6871–6899 (2023). https://doi.org/10.1007/s00034-023-02430-2

    Article  Google Scholar 

  9. Yeswanth, P.V., Khandelwal, R., Deivalakshmi, S.: Super resolution-based leaf disease detection in potato plant using broad deep residual network (BDRN). SN Comput. Sci. 4(2), 112 (2022)

    Article  Google Scholar 

  10. Yeswanth, P.V., Deivalakshmi, S.: Extended wavelet sparse convolutional neural network (EWSCNN) for super resolution. Sādhanā 48(2), 52 (2023)

    Article  MathSciNet  Google Scholar 

  11. Chauhan, A., Chauhan, D., Rout, C.: Role of gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation. PLoS ONE 9, e112980 (2014). https://doi.org/10.1371/journal.pone.0112980

    Article  Google Scholar 

  12. Liu, C., et al.: TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network. In: 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 2314–2318 (2017). https://doi.org/10.1109/ICIP.2017.8296695

  13. Yeswanth, P.V., Raviteja, R., Deivalakshmi, S.: Sovereign Critique Network (SCN) Based Super-Resolution for chest X-rays images. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India, pp. 1–5 (2023). https://doi.org/10.1109/IConSCEPT57958.2023.10170157

  14. Norval, M., Wang, Z., Sun, Y.: Pulmonary Tuberculosis Detection Using Deep Learning Convolutional Neural Networks, pp. 47–51 (2019). https://doi.org/10.1145/3376067.3376068

  15. Rahman, T., et al.: Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8, 191586–191601 (2020). https://doi.org/10.1109/ACCESS.2020.3031384

    Article  Google Scholar 

  16. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)

    Google Scholar 

  17. Kim, J.-H., Choi, J.-H., Cheon, M., Lee, J.-S.: RAM: residual attention module for single image super-resolution. arXiv preprint arXiv:1811.12043 (2018)

  18. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18

    Chapter  Google Scholar 

  19. Yeswanth, P.V., Kushal, S., Tyagi, G., Kumar, M.T., Deivalakshmi, S., Ramasubramanian, S.P.: Iterative super resolution network (ISNR) for potato leaf disease detection. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India, pp. 1–6 (2023). https://doi.org/10.1109/IConSCEPT57958.2023.10170224

  20. Ahsan, M., Gomes, R., Denton, A.: Application of a convolutional neural network using transfer learning for tuberculosis detection. In: Proceedings of the IEEE International Conference Electro Information Technology (EIT), pp. 427–433, May 2019

    Google Scholar 

  21. Yeswanth, P.V., Khandelwal, R., Deivalakshmi, S.: Two fold extended residual network based super resolution for potato plant leaf disease detection. In: Misra, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Patel, A. (eds.) ICIoTCT 2022. LNCS, vol. 616, pp. 197–209. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-9719-8_16

  22. Yeswanth, P.V., Rajan, J., Mantha, B.P., Siva, P., Deivalakshmi, S.: Self-Governing Assessment Network (SGAN) based super-resolution for CT chest images. In: 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), Srinagar Garhwal, India, pp. 1–5 (2023). https://doi.org/10.1109/IC2E357697.2023.10262573

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Acknowledgement

Authors are thankful to the Director of the National Institute of Technology - Tiruchirappalli for granting us permission to use the GPU resources from the Center of Excellence – Artificial Intelligence (CoE-AI) lab.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tiruchirappalli, India

    P. V. Yeswanth, Kunal Vijay Thool & S. Deivalakshmi

Authors
  1. P. V. Yeswanth
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  2. Kunal Vijay Thool
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  3. S. Deivalakshmi
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Corresponding author

Correspondence to P. V. Yeswanth .

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Editors and Affiliations

  1. Indraprastha Institute of Information Technology, Delhi, India

    Vikram Goyal

  2. University of Delhi, Delhi, India

    Naveen Kumar

  3. Nanyang Technological University, Singapore, Singapore

    Sourav S. Bhowmick

  4. Indian Institute of Technology, Kharagpur, India

    Pawan Goyal

  5. Birla Institute of Technology and Science, Pilani, India

    Navneet Goyal

  6. Indraprastha Institute of Information Technology, Delhi, India

    Dhruv Kumar

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Yeswanth, P.V., Thool, K.V., Deivalakshmi, S. (2023). Tuberculosis Disease Diagnosis Using Controlled Super Resolution. In: Goyal, V., Kumar, N., Bhowmick, S.S., Goyal, P., Goyal, N., Kumar, D. (eds) Big Data and Artificial Intelligence. BDA 2023. Lecture Notes in Computer Science, vol 14418. Springer, Cham. https://doi.org/10.1007/978-3-031-49601-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-49601-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49600-4

  • Online ISBN: 978-3-031-49601-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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