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Proceedings Paper

Decision fusion on image analysis and tympanometry to detect eardrum abnormalities
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Paper Abstract

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.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141M (16 March 2020); doi: 10.1117/12.2549394
Show Author Affiliations
Hamidullah Binol, Wake Forest School of Medicine (United States)
Aaron C. Moberly, The Ohio State Univ. (United States)
M. Khalid Khan Niazi, Wake Forest School of Medicine (United States)
Garth Essig, The Ohio State Univ. (United States)
Jay Shah, Case Western Reserve Univ. (United States)
Charles Elmaraghy, The Ohio State Univ. (United States)
Theodoros Teknos, The Ohio State Univ. (United States)
Nazhat Taj-Schaal, The Ohio State Univ. (United States)
Lianbo Yu, The Ohio State Univ. (United States)
Metin N. Gurcan, Wake Forest School of Medicine (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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