Additive margin networks with adaptive feature recalibration and its applications in Brain Stroke CT Image classification

Autor:	Qinling Gao, Hao Fu, Xuejing Zhao, Zhaoming Ge
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	brain image recognition Convolutional neural networks Additive margin softmax loss Adaptive feature recalibration Photography TR1-1050 Computer software QA76.75-76.765
Zdroj:	IET Image Processing, Vol 18, Iss 3, Pp 731-740 (2024)
Druh dokumentu:	article
ISSN:	1751-9667 1751-9659
DOI:	10.1049/ipr2.12981
Popis:	Abstract In this paper, an additive margin network with adaptive feature recalibration, AMNets, for handling a wide range of visual tasks, is proposed. The new AMNets consists of three improvements on convolutional neural networks: (1) squeeze‐and‐excitation mechanism was embedded into the the residual block of single layer to improve the performance of the classification, (2) softmax loss was replaced by the additive margin softmax loss to minimize intra‐class variance and maximize inter‐class variance, and (3) Adam optimizer was exploited to update iteratively the network parameters. Experiments on the Brain Stroke CT Image Dataset show that our additive margin network is quite effective to improve state‐of‐the‐art algorithms.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/a81e8f2952fa4eddb6e0ec95b72da779 Zobrazit plný text záznamu Plný text View record in DOAJ