mlsanityData to 13 Oct 2026

CNN-based Classification Framework for Lung Tissues with Auxiliary Information

arXiv 2206.06701 ↗First posted 14 Jun 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formulating treatment plans. Although previous work has produced impressive results in classifying interstitial lung diseases, there is still room for improving the accuracy of these techniques, mainly to enhance automated decision-making. In order to improve the classification precision, our study proposes a convolutional neural networks-based framework with auxiliary information. Firstly, ILD images are added with their medical information by re-scaling the original image in Hounsfield Units. Secondly, a modified CNN model is used to produce a vector of classification probability for each tissue. Thirdly, location information of the input image, consisting of the occurrence frequencies of different diseases in the CT scans on certain locations, is used to calculate a location weight vector. Finally, the Hadamard product between two vectors is used to produce a decision vector for the prediction. Compared to the state-of-the-art methods, the results using a publicly available ILD database show the potential of predicting these using different auxiliary information.

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References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,457
  4. 2014Going Deeper with Convolutionscited by 4,239
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,326
  6. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 221
  7. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  8. 2020Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learningcited by 67
  9. 2015Improving Image Classification with Location Contextcited by 33
  10. 2016Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensitiescited by 13
  11. 2018Improving Image Clustering With Multiple Pretrained CNN Feature Extractorscited by 12
  12. 2016Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysiscited by 6
1 more
  1. 2017Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Poolingcited by 2

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