Literature Review on Breast Cancer Diagnosis using 3D Images: Methods and Performance Analysis

Umamaheswari, Thippaluru; Babu, Y. Murali Mohan

doi:10.1109/icosec51865.2021.9591861

Cited by 2 publications

(1 citation statement)

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“…Learning that takes place based on partly labeled datasets is called semi-supervised learning (Van Engelen and Horqos, 2020 ; Umamaheswari and Babu, 2021 ). DRL and generative adversarial networks (GANs) are the most well-known networks that exploit semi-supervised learning scenarios.…”

Section: Vision-based Deep Learning Systemmentioning

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: Vision-based Deep Learning Systemmentioning

confidence: 99%