In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi163-vi164
Abstract:
convolutional neural network (CNN) model using MRI imaging are likely to be effective for grading glioma. However, interpretation of the judgment basis of CNN is difficult. The purpose of this study is twofold. One is to create a high accuracy machine learning model that grading of glioma (Glioma grade II, between III and IV). The other is to visualize the judgment basis of the model. METHODS We targeted cases that were imaged at our Hospital during the period from August 2014 to January 2018. Five types of MRI are used. The five types are two types of DWI (b1000DWI and b2000DWI, respectively, with a value of 1000 and 2000), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and mean kurtosis (MK). The images were input to CNN wtihout ROI. Seven types of CNN were prepared, and 100 epochs of learning were performed. RESULTS 55 cases were included. There were 14 grade II, 12 grade III, and 29 grade IV). Of these, 44 cases up to July 2017 in chronological order were used as a dataset for learning, and 11 cases from August 2017 to January 2018 were taken as independent test dataset. The best results were obtained using MK as input, with a percentage of correct cases of test data of 0.82. Subsequently, we applied Grad-CAM to the model. Grad-CAM is visualized technique that shows where CNN focused on. The model focused on the tumor part of the image. In addition, in the cases of glioma and meningioma coexisting, grading was performed focusing on the glioma area not on meningioma area. CONCLUSION When grading glioma, it was considered that CNN decide glioma’s grade focusing on tumor part of the image, as well as humans.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noz175.683
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2019
detail.hit.zdb_id:
2094060-9
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