AM-DenseNet:A Novel DenseNet Framework using Attention Mechanisms for COVID-19 CT Image Classification

Shi, Yujiao; Zhou, Tunan; You, Zeqing; Chu, Jielei; Li, Tianrui

doi:10.1109/ccis57298.2022.10016401

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…GoogLeNet [18] used convolutional kernels of different sizes to design convolutional models of depth and width. ResNet [19] and DenseNet [20] [22]. With the improvement of model accuracy, more and more scholars are applying deep learning to other fields such as the power industry [23,24].…”

Section: Industrial Part Classificationmentioning

confidence: 99%

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A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

An,

Sun,

Song

et al. 2023

IET Image Processing

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Traditional warehousing typically needs machine learning or manual tagging to classify objects. However, this method is less robust and consumes a lot of labour and material resources. Based on DenseNet, this work proposes a feature weighting convolutional network recognition model and designs a set of software and hardware for data acquisition, which is applied to the efficient classification of industrial parts in warehouse management. Firstly, this work modifies DenseNet by embedding SE‐Block, and replaces the cross‐entropy loss function with the focus loss function to optimize the model structure. Secondly, a multi‐view hardware and software acquisition system is designed to complete the functions of part image acquisition, image preprocessing, model training and part recognition. Finally, an industrial parts sorting experiment was designed. Compared with the original DenseNet model, the proposed weighted convolutional network identification model showed that the accuracy of the modified model was increased by 3.09% and the convergence rate was significantly improved. The modified model proposed in this work aims to improve the recognition accuracy of industrial parts in modern warehouse management, so as to modify the classification efficiency of warehouse parts in production.

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Section: Industrial Part Classificationmentioning

confidence: 99%

“…Jiao et al. present a novel DenseNet framework with attention mechanisms (AM‐DenseNet) to extract lung feature of 1 COVID‐19 patient [22]. With the improvement of model accuracy, more and more scholars are applying deep learning to other fields such as the power industry [23, 24].…”

Section: Related Workmentioning

confidence: 99%