A multi-label classification model for full slice brain computerised tomography image
- PDF / 3,385,669 Bytes
- 18 Pages / 595 x 794 pts Page_size
- 6 Downloads / 151 Views
RESEARCH
Open Access
A multi-label classification model for full slice brain computerised tomography image Jianqiang Li1,2 , Guanghui Fu1,2 , Yueda Chen3 , Pengzhi Li1,2 , Bo Liu1,2 , Yan Pei4*
and Hui Feng1,2
From 15th International Symposium on Bioinformatics Research and Applications (ISBRA’19) Barcelona, Spain. 3-6 June 2019 *Correspondence: [email protected] 4 Computer Science Division, University of Aizu, Aizuwakamatsu 965-8580, Japan Full list of author information is available at the end of the article
Abstract Background: Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results: In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion: The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify (Continued on next page)
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the artic
Data Loading...
