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Medical Image Classification based on an Adaptive Size Deep Learning Model

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Published:26 October 2021Publication History
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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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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 3s
      October 2021
      324 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3492435
      Issue’s Table of Contents

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      Publication History

      • Published: 26 October 2021
      • Accepted: 1 May 2021
      • Revised: 1 April 2021
      • Received: 1 November 2020
      Published in tomm Volume 17, Issue 3s

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