A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images

Research article (Biomedical Signal Processing and Control, 2020) · cited 77× · AI/ML
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A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images

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A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-deep-learning-model-using-data-augmentation-for-detection-of-architectural-distortion-in-whole-and-patches-of-images
MLA “A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-deep-learning-model-using-data-augmentation-for-detection-of-architectural-distortion-in-whole-and-patches-of-images.
BibTeX @misc{4ortxyz_a-deep-learning-model-using-data-augmentation-for-detection-of-architectural-distortion-in-whole-and-patches-of-images_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images}}, year = {2026}, url = {https://4ort.xyz/entity/a-deep-learning-model-using-data-augmentation-for-detection-of-architectural-distortion-in-whole-and-patches-of-images}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images — https://4ort.xyz/entity/a-deep-learning-model-using-data-augmentation-for-detection-of-architectural-distortion-in-whole-and-patches-of-images (retrieved 2026-05-24)

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