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Medical Image Classification based on an Adaptive Size Deep Learning Model
Acm Transactions on Multimedia Computing Communications and Applications ; 17(3):18, 2021.
Article in English | Web of Science | ID: covidwho-1622095
ABSTRACT
With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: Acm Transactions on Multimedia Computing Communications and Applications Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: Acm Transactions on Multimedia Computing Communications and Applications Year: 2021 Document Type: Article