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Proceedings Paper

Detection of eardrum abnormalities using ensemble deep learning approaches
Author(s): Caglar Senaras; Aaron C. Moberly; Theodoros Teknos; Garth Essig; Charles Elmaraghy; Nazhat Taj-Schaal; Lianbo Yua; Metin N. Gurcan
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Paper Abstract

In this study, we proposed an approach to report the condition of the eardrum as “normal” or “abnormal” by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(± 12.1%) and 82.6% (± 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (± 10.3%).

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751A (27 February 2018); doi: 10.1117/12.2293297
Show Author Affiliations
Caglar Senaras, The Ohio State Univ. (United States)
Aaron C. Moberly, The Ohio State Univ. (United States)
Theodoros Teknos, The Ohio State Univ. (United States)
Garth Essig, The Ohio State Univ. (United States)
Charles Elmaraghy, The Ohio State Univ. (United States)
Nazhat Taj-Schaal, Ohio State Univ. College of Medicine (United States)
Lianbo Yua, The Ohio State Univ. (United States)
Metin N. Gurcan, Wake Forest School of Medicine (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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