Classifying SPECT bone scan images using improved convolutional neural networks

Yubo Wang; Qiang Lin; Xu Cao; Yongchun Cao; Zhengxing Man

doi:10.1117/12.2643915

9 October 2022 Classifying SPECT bone scan images using improved convolutional neural networks

Yubo Wang, Qiang Lin, Xu Cao, Yongchun Cao, Zhengxing Man

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 1224610 (2022) https://doi.org/10.1117/12.2643915
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

SPECT imaging is an effective medical method for diagnosis, treatment, evaluation, and prevention of a range of serious diseases and medical conditions. SPECT bone scans have the potential to provide more accurate assessment of disease staging and severity. To classify medical images more effectively, in this work, we propose an AlexNet-based image classification model. Specifically, the network structure of AlexNet is fine-tuned and attention mechanism is added to construct an improved model. Lastly, experimental results show that the improved AlexNet classification model is feasible for classifying SEPCT bone scan images, with obtaining scores of 0.7377, 0.7677, 0.7723, and 0.7674 for Acc, Pre, Rec and F-1, respectively.

Citation Download Citation

Yubo Wang, Qiang Lin, Xu Cao, Yongchun Cao, and Zhengxing Man "Classifying SPECT bone scan images using improved convolutional neural networks", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 1224610 (9 October 2022); https://doi.org/10.1117/12.2643915

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