Classifying Bone Metastasized SPECT Images in RGB Format with VGGNets

Chen, Xiaoyan; Zheng, Ziwen; Liu, Liangxia; Wang, Weili; Lin, Qiang

doi:10.1109/cisce52179.2021.9446035

2021 International Conference on Communications, Information System and Computer Engineering (CISCE) 2021

DOI: 10.1109/cisce52179.2021.9446035

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Classifying Bone Metastasized SPECT Images in RGB Format with VGGNets

Xiaoyan Chen

Ziwen Zheng

Liangxia Liu

et al.

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2024

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AI‐powered automated analysis of bone scans: A survey

Lin,

He,

Guo

2024

IET Image Processing

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As one of the key techniques of artificial intelligence, deep learning has emerged as an effective approach for analysing medical images. Various imaging techniques including the planar bone scintigraphy, single photon emission computed tomography and PET can be used to evaluate, in vivo, bone conditions. The introduction of deep learning techniques especially the convolutional neural networks can significantly improve diagnosis accuracy and efficiency of nuclear medicine physicians. Focusing on bone scans acquired by various nuclear medicine imaging techniques, his paper reviews existing work on deep learning‐based classification, segmentation and object detection of bone scans. Specifically, an overview of existing work about research objective is presented, deep learning models are adopted, and main results are achieved. Research challenges and directions for developing automated analysis of bone scans with deep learning techniques are then discussed.

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AI‐powered automated analysis of bone scans: A survey

Lin,

He,

Guo

2024

IET Image Processing

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Classifying Bone Metastasized SPECT Images in RGB Format with VGGNets

Cited by 1 publication

References 1 publication

AI‐powered automated analysis of bone scans: A survey

AI‐powered automated analysis of bone scans: A survey

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