27 February 2018 Detection of eardrum abnormalities using ensemble deep learning approaches
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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%).
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Caglar Senaras, Caglar Senaras, Aaron C. Moberly, Aaron C. Moberly, Theodoros Teknos, Theodoros Teknos, Garth Essig, Garth Essig, Charles Elmaraghy, Charles Elmaraghy, Nazhat Taj-Schaal, Nazhat Taj-Schaal, Lianbo Yua, Lianbo Yua, Metin N. Gurcan, 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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