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Improve the Diagnosis on Fundus Photography with Deep Transfer Learning

Abstract Details

2021, Master of Sciences, Case Western Reserve University, EECS - Computer and Information Sciences.
Fundus photography-based eye disease prediction attracted great attention since breakthroughs in deep convolutional neuron networks (DCNNs). However, the performance of existing studies focusing on identifying the right disease among several candidates,which is close to clinical diagnosis in practice,is at most mediocre. Moreover, obtaining large labeled dataset is difficult due to privacy concerns, resulting in the infeasibility to train huge DCNNs. Hence, we propose to utilize a lightweight deep learning architecture (MobileNetV2) and transfer learning to distinguish four eye diseases from normal controls using a small dataset. A visualization approach is also applied to highlight the loci for the predicted label, which may give some hints for further fundus image studies. Our experimental results show that our system achieves an average accuracy of 96.2%, sensitivity of 90.4%, and specificity of 97.6% via five independent runs, and outperforms two other deep learning based algorithms both in accuracy and efficiency.
Jing Li (Advisor)
Guiyun Wu (Committee Member)
Shuai Xu (Committee Member)
66 p.

Recommended Citations

Citations

  • Guo, C. (2021). Improve the Diagnosis on Fundus Photography with Deep Transfer Learning [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1621674781655785

    APA Style (7th edition)

  • Guo, Chen. Improve the Diagnosis on Fundus Photography with Deep Transfer Learning. 2021. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1621674781655785.

    MLA Style (8th edition)

  • Guo, Chen. "Improve the Diagnosis on Fundus Photography with Deep Transfer Learning." Master's thesis, Case Western Reserve University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1621674781655785

    Chicago Manual of Style (17th edition)