Open Access
Issue
E3S Web Conf.
Volume 619, 2025
3rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)
Article Number 02013
Number of page(s) 13
Section Innovations in Power Systems and Grid Infrastructure
DOI https://doi.org/10.1051/e3sconf/202561902013
Published online 12 March 2025
  1. Harvey, Hugh, Andreas Heindl, Galvin Khara, Dimitrios Korkinof, Michael O’Neill, Joseph Yearsley, Edith Karpati, Tobias Rijken, Peter Kecskemethy, and Gabor Forrai. “Deep learning in breast cancer screening.” Artificial intelligence in medical imaging: opportunities, applications and risks (2019): 187-215. [Google Scholar]
  2. Chang, Ken &Balachandar, Niranjan& Lam, Carson & Yi, Darvin& Brown, James & Beers, Andrew & Rosen, Bruce & Rubin, Daniel &Kalpathy-Cramer, Jayashree. (2018). Distributed deep learning networks among institutions for medical imaging. Journal of the American Medical Informatics Association: JAMIA. 25. 10.1093/jamia/ocy017 [Google Scholar]
  3. Wang, H., Zhou, Z., Li, Y. et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F- FDG PET/CT images. EJNMMI Res 7, 11 (2017). https://doi.org/10.1186/s13550-017- 0260-9 [CrossRef] [PubMed] [Google Scholar]
  4. Krizhevsky, A., I. Sutskever, and G. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” Neural Information Processing Systems 25 (1): 84–90. https://doi.org/10.1145/3065386 [Google Scholar]
  5. A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” 2018 International Interdisciplinary PhD Workshop (IIPhDW), Świnouście, Poland, 2018, pp. 117-122, doi: 10.1109/IIPHDW.2018.8388338. [Google Scholar]
  6. Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017 Aug;284(2):574-582. DOI: 10.1148/radiol.2017162326. PMID: 28436741. [CrossRef] [Google Scholar]
  7. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216. PMID: 27898976. [CrossRef] [PubMed] [Google Scholar]
  8. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist- level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25. Erratum in: Nature. 2017 Jun 28;546(7660):686. PMID: 28117445; PMCID: PMC8382232. [CrossRef] [PubMed] [Google Scholar]
  9. Yun J, Park JE, Lee H, Ham S, Kim N, Kim HS. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Sci Rep. 2019 Apr 5;9(1):5746. doi: 10.1038/s41598-019-42276-w. PMID: 30952930; PMCID: PMC6451024. [CrossRef] [Google Scholar]
  10. S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017, doi: 10.1109/TPAMI.2016.2577031 [CrossRef] [Google Scholar]
  11. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779-788, doi: 10.1109/CVPR.2016.91. [Google Scholar]
  12. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6517-6525, doi: 10.1109/CVPR.2017.690. [Google Scholar]
  13. R. Girshick, J. Donahue, T. Darrell and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 580-587, doi: 10.1109/CVPR.2014.81. [Google Scholar]
  14. Yang S, Sun Y, Chen Y, Jiao L (2012) Structural similarity regularized, and sparse coding based super-resolution for medical images. Biomed Signal Process Control 7(6):579–590 [CrossRef] [Google Scholar]
  15. A. Bovik, The Essential Guide to the Image Processing, Academic Press, New York, NY, USA, 2009. [Google Scholar]
  16. Honggang Chen, Xiaohai He, Linbo Qing, QizhiTeng, Chao Ren, SGCRSR: Sequential gradient constrained regression for single image super-resolution, Signal Processing: Image Communication, Volume 66, 2018,Pages 1-18,ISSN 0923-5965. https://doi.org/10.1016/j.image.2018.04.012. [CrossRef] [Google Scholar]
  17. S. Esedoglu and S. J. Osher, “Decomposition of images by the anisotropic Rudin- Osher-Fatemi model,” Communications on Pure and Applied Mathematics, vol. 57, no. 12, pp. 1609–1626, 2004. [CrossRef] [Google Scholar]
  18. M. R. Banham and A. K. Katsaggelos, “Digital image restoration,” in IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24-41, March 1997, doi: 10.1109/79.581363. [CrossRef] [Google Scholar]
  19. R. C. Gonzalez and R. E. Woods, Digital Image Processing Prentice Hall, 3rd edition, 2007 [Google Scholar]
  20. Puttagunta, M., Ravi, S. Medical image analysis based on deep learning approach. Multimed Tools Appl 80, 24365–24398 (2021). https://doi.org/10.1007/s11042-021- 10707-4 [CrossRef] [PubMed] [Google Scholar]
  21. Pawan Kumar Mall, Pradeep Kumar Singh, Swapnita Srivastav, Vipul Narayan, Marcin Paprzycki, Tatiana Jaworska, Maria Ganzha, A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities, Healthcare Analytics, Volume 4, 2023, 100216, ISSN 2772-4425, https://doi.org/10.1016/j.health.2023.100216. [CrossRef] [Google Scholar]
  22. Predrag S. Stanimirović, Igor Stojanović, Vasilios N. Katsikis, Dimitrios Pappas, Zoran Zdravev, “Application of the Least Squares Solutions in Image Deblurring”, Mathematical Problems in Engineering, vol. 2015, Article ID 298689, 18 pages, 2015. https://doi.org/10.1155/2015/298689 [Google Scholar]
  23. S. Jose, N. Mohan, V. Sowmya and K. P. Soman, “Least square based image deblurring,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017, pp. 1453-1457, doi: 10.1109/ICACCI.2017.8126045. [Google Scholar]
  24. Devi, M. Kalpana and R. Ashwini. “An Analysis on Implementation of Various Deblurring Techniques in Image Processing.” (2016). [Google Scholar]
  25. S. F. M. Hussain, R. Karthikeyan, S. Ramamoorthi, I. S. Arafat and S. S. M. Gani, “Denial of Service Attack Analysis Using Machine Learning Techniques,” 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, 2023, pp. 886-896, doi: 10.1109/ICECAA58104.2023.10212215. [Google Scholar]
  26. Z. Luo, H. Huang, L. Yu, Y. Li, H. Fan and S. Liu, “Deep Constrained Least Squares for Blind Image Super-Resolution,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 17621-17631, doi: 10.1109/CVPR52688.2022.01712. [Google Scholar]
  27. R. Karthikeyan, B. Sundaravadivazhagan, Robin Cyriac, Praveen Kumar Balachandran, S. Shitharth, “Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems”, Mobile Information Systems, vol. 2023, Article ID 7754765, 11 pages, 2023. https://doi.org/10.1155/2023/7754765. [CrossRef] [Google Scholar]
  28. Venkatesan, Karunakaran, Pramod Kumar Gouda, Bibhuti Bhusan Rath, and Murugaperunal Krishnamoorthy. “Optimal day-ahead scheduling of microgrid equipped with electric vehicle and distributed energy resources: SFO-CSGNN approach.” Journal of Energy Storage 102 (2024): 113933. [CrossRef] [Google Scholar]
  29. Medical image denoising and classification based on machine learning: A review, Aumya Chaturvedi1, Aditya Sai Srinivas T2, Karthikeyan R3, Vijayaraj M3, Nirmal Kumar A4 and Sangeetha M5,2022 ECS - The Electrochemical Society, ECS Transactions, Volume 107, Number 1,DOI 10.1149/10701.6111ecst. [Google Scholar]
  30. Murali, Srikanth & Krishnan, K. & Vishvanathan, Sowmya & Kp, Soman. (2017). Image denoising based on weighted regularized least square method. 1-5. 10.1109/ICCPCT.2017.8074388. [Google Scholar]
  31. Mishra, Sidharth & Sarkar, Uttam & Taraphder, Subhash & Datta, Sanjoy & Swain, Devi & Saikhom, Reshma& Panda, Sasmita & Laishram, Menalsh. (2017). Principal Component Analysis. International Journal of Livestock Research. 1. 10.5455/ijlr.20170415115235. [Google Scholar]
  32. Howley, Tom & Madden, Michael & O’Connell, Marie-Louise & Ryder, Alan. (2005). The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data. 209-222. 10.1007/1-84628-224-1_16. [Google Scholar]

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