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High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model

journal contribution
posted on 2020-03-27, 12:27 authored by Yu-Dong Zhang, Vishnu V. Govindaraj, Chaosheng Tang, Weiguo Zhu, Junding Sun
Aim: We originated a high-performance multiple sclerosis classification model in this study. Method: The dataset was segmented into training, validation, and test sets. We used AlexNet as the basis model, and employed transferred learning to adapt AlexNet to classify multiple sclerosis brain image in our task. We tested different settings of transfer learning, i.e., how many layers were transferred and how many layers were replaced. The learning rate of replaced layers are 10 times of that of transferred layer. We compare the results using five measures: sensitivity, specificity, precision, accuracy and F1 score. Results: We found replacing the FC_8 block in original AlexNet can procure the best performance: a sensitivity of 98.12%, a specificity of 98.22%, an accuracy of 98.17%, a precision of 98.21%, and an F1 score of 98.15%. Conclusions: Our performance is better than seven state-of-the-art multiple sclerosis classification approaches.

History

Citation

Journal of Medical Imaging and Health Informatics, Volume 9, Number 9, December 2019, pp. 2012-2021(10)

Author affiliation

Department of Informatics

Published in

JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS

Volume

9

Issue

9

Pagination

2012 - 2021

Publisher

AMER SCIENTIFIC PUBLISHERS

issn

2156-7018

eissn

2156-7026

Copyright date

2019

Available date

2019-12-01

Publisher version

https://www.ingentaconnect.com/content/asp/jmihi/2019/00000009/00000009/art00035#expand/collapse

Notes

the publisher does not allow archiving of the accepted manuscript

Language

English