Dr. Hamidullah Binol Profile

Dr. Hamidullah Binol

at Wake Forest School of Medicine

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Author

Publications (8)

Proceedings Article | 15 February 2021 Presentation + Paper

Automated video summarization and label assignment for otoscopy videos using deep learning and natural language processing

Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron Moberly, Metin Gurcan

Proceedings Volume 11601, 116010S (2021) https://doi.org/10.1117/12.2582009

KEYWORDS: Video, Video processing, Diagnostics, Visualization, Ear, Visual process modeling, Instrument modeling, Image segmentation, Composites, Visual analytics

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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 | 16 March 2020 Presentation + Paper

A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification

Hamidullah Binol, M. Khalid Khan Niazi, Alisha Plotner, Jennifer Sopkovich, Benjamin Kaffenberger, Metin Gurcan

Proceedings Volume 11314, 1131415 (2020) https://doi.org/10.1117/12.2549392

KEYWORDS: Visualization, Convolutional neural networks, Neural networks, Computer aided diagnosis and therapy, Data centers, Dermatology, Machine vision, Computer vision technology, Algorithm development, Health informatics, Image analysis, Pattern recognition

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Proceedings Article | 17 May 2016 Paper

Covariance descriptor fusion for target detection

Huseyin Cukur, Hamidullah Binol, Abdullah Bal, Fatih Yavuz

Proceedings Volume 9842, 98421B (2016) https://doi.org/10.1117/12.2223765

KEYWORDS: Target detection, Hyperspectral imaging, Image fusion, Sensors, Data modeling, Detection and tracking algorithms, Matrices, Hyperspectral target detection, Critical dimension metrology, Associative arrays

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Proceedings Article | 17 May 2016 Paper

Food inspection using hyperspectral imaging and SVDD

Faruk Sukru Uslu, Hamidullah Binol, Abdullah Bal

Proceedings Volume 9864, 98640N (2016) https://doi.org/10.1117/12.2223938

KEYWORDS: Food inspection, Hyperspectral imaging, Visualization, Safety, Nondestructive evaluation, Inspection, Skin, Tumors, Optical spheres, Binary data

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Proceedings Article | 17 May 2016 Paper

Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

Hamidullah Binol, Abdullah Bal

Proceedings Volume 9842, 98421G (2016) https://doi.org/10.1117/12.2223917

KEYWORDS: Target detection, Hyperspectral imaging, Hyperspectral target detection, Sensors, Feature extraction, Feature selection, Matrices, Detection and tracking algorithms, Roads, Target recognition

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Proceedings Article | 20 October 2015 Paper

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Hamidullah Binol, Abdullah Bal, Huseyin Cukur

Proceedings Volume 9646, 96460T (2015) https://doi.org/10.1117/12.2197924

KEYWORDS: Matrices, Detection and tracking algorithms, Hyperspectral imaging, Target detection, Optimization (mathematics), Electronics, Communication engineering, Hyperspectral target detection, Infrared detectors, Infrared imaging

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Proceedings Article | 15 October 2015 Paper

An effective band selection approach for classification in remote sensing imagery

Hüseyin Cukur, Hamidullah Binol, Faruk Uslu, Abdullah Bal

Proceedings Volume 9643, 96432O (2015) https://doi.org/10.1117/12.2197108

KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Image classification, Remote sensing, Dimension reduction, Spectral resolution, Visible radiation, Infrared imaging, Infrared radiation, Image processing

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Showing 5 of 8 publications

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