27 February 2018 Detection of eardrum abnormalities using ensemble deep learning approaches
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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%).
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Caglar Senaras, Aaron C. Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yua, Metin N. Gurcan, "Detection of eardrum abnormalities using ensemble deep learning approaches", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751A (27 February 2018); doi: 10.1117/12.2293297; https://doi.org/10.1117/12.2293297
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