Dr. Lianbo Yu Profile

Dr. Lianbo Yu

at Ohio State Univ

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Publications (3)

Proceedings Article | 16 March 2020 Presentation + Paper

Decision fusion on image analysis and tympanometry to detect eardrum abnormalities

Hamidullah Binol, Aaron Moberly, M. Khalid Khan Niazi, Garth Essig, Jay Shah, Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, Metin Gurcan

Proceedings Volume 11314, 113141M (2020) https://doi.org/10.1117/12.2549394

KEYWORDS: Ear, Image analysis, Video, Image classification, Visualization, Medicine, Sensors, Diagnostics, Decision support systems, Pattern recognition, Machine learning, Machine vision, Computer aided diagnosis and therapy

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Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.

Proceedings Article | 27 February 2018 Paper

Detection of eardrum abnormalities using ensemble deep learning approaches

Caglar Senaras, Aaron C. Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yua, Metin Gurcan

Proceedings Volume 10575, 105751A (2018) https://doi.org/10.1117/12.2293297

KEYWORDS: Neural networks, Bragg cells, Network architectures, Convolutional neural networks, Ear, Medicine, Diagnostics, Machine learning, Health informatics, Machine vision

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Proceedings Article | 3 March 2017 Presentation + Paper

Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features

Caglar Senaras, Aaron Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yu, Metin Gurcan

Proceedings Volume 10134, 101341X (2017) https://doi.org/10.1117/12.2250592

KEYWORDS: Image analysis, Diagnostics, Ear, Image classification, Feature extraction, Machine vision, Computer vision technology, Bragg cells, Video

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