Transfer Learning Models in Medical Image Anomaly Detection
DOI:
https://doi.org/10.32628/IJSRST251222678Abstract
Transfer learning is a common method for moving information from one field to another. In medical imaging applications, transfer from ImageNet has emerged as the de-facto method, in spite of variations in the requirements and picture properties among the domains. The elements that define the usefulness of transfer learning to the medical field are unknown, nevertheless. Recently, the long-held belief that features from the source domain are reused has come under scrutiny.
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Alammar Z, Alzubaidi L, Zhang J et al (2023) Deep transfer learning with enhanced feature fusion for detection of abnormalities in X-ray images. Cancers (Basel) 15:4007.
Ali MU, Kallu KD, Masood H et al (2023) A CNN-based chest infection diagnostic model: a multistage multiclass isolated and developed transfer learning framework. Int J Intell Syst 2023.
Ali NF, Atef M (2022) LSTM multi-stage transfer learning for blood pressure estimation using photoplethysmography. Electronics 11:3749.
Alowais SA, Alghamdi SS, Alsuhebany N et al (2023) Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23:689.
Al-Stouhi S, Reddy CK (2016) Transfer learning for class imbalance problems with inadequate data. Knowl Inf Syst 48:201–228.
Alsubaie MG, Luo S, Shaukat K (2024) Alzheimer’s disease detection using deep learning on neuroimaging: a systematic review. Mach Learn Knowl Extr 6:464–505.
Alzubaidi L, Fadhel MA, Al-Shamma O et al (2020) Towards a better understanding of transfer learning for medical imaging: a case study. Appl Sci 10:4523.
Alzubaidi L, Zhang J, Humaidi AJ et al (2021a) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Springer International Publishing, Cham
Alzubaidi L, Zhang J, Humaidi AJ et al (2021b) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.
Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely Connected Convolutional Networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 2261–2269.
Liu, Z.; Mao, H.; Wu, C.Y.; Feichtenhofer, C.; Darrell, T.; Xie, S. A ConvNet for the 2020s. In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18–24 June 2022; pp. 11966–11976.
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015; pp. 1–9.
Yann, L.; Patrick, H.; Bottou, L.; Yoshua, B. Object recognition with gradient-based learning. In Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 1999; Volume 1681, pp. 319–345
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