Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume

Kim, Dae Hoe; Kim, Seong-Tae; Baddar, Wissam J.; Ro, Yong Man

doi:10.1109/icip.2015.7351662

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…To learn the bilateral features from the Digital Breast Tom (DBT) synthesis, there are three-dimensional multi-views introduced in the model (Kim et al, 2015 ). A volume of interest (VOI) was achieved from the source volume, which was managed in the registered target as an individual input rather than a VOI.…”

Section: Cancer Overviewmentioning

confidence: 99%

A Survey on Human Cancer Categorization Based on Deep Learning

Ahmad

Mohamed

Maher

et al. 2022

Front. Artif. Intell.

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In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

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Section: Cancer Overviewmentioning

confidence: 99%