An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions

Ozmen, Goktug C.; Gazi, Asim H.; Gharehbaghi, Sevda; Richardson, Kim; Safaei, Mohsen; Whittingslow, Daniel C.; Prahalad, Sampath; Hunnicutt, Jennifer L.; Xerogeanes, John W.; Snow, Teresa K.; Inan, Omer T.

doi:10.1007/s10439-021-02788-x

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…The importance of data augmentation cannot be overemphasized, as it has played a major role in obtaining state-of-the-art results. Another study by the authors in [124] applied the window removal method to increase training samples and improve knee health classification.…”

Section: Data Augmentation Methods In Sound Classificationmentioning

confidence: 99%

Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

et al. 2022

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The aim of this systematic literature review (SLR) is to identify and critically evaluate current research advancements with respect to small data and the use of data augmentation methods to increase the amount of data available for deep learning classifiers for sound (including voice, speech, and related audio signals) classification. Methodology: This SLR was carried out based on the standard SLR guidelines based on PRISMA, and three bibliographic databases were examined, namely, Web of Science, SCOPUS, and IEEE Xplore. Findings. The initial search findings using the variety of keyword combinations in the last five years (2017–2021) resulted in a total of 131 papers. To select relevant articles that are within the scope of this study, we adopted some screening exclusion criteria and snowballing (forward and backward snowballing) which resulted in 56 selected articles. Originality: Shortcomings of previous research studies include the lack of sufficient data, weakly labelled data, unbalanced datasets, noisy datasets, poor representations of sound features, and the lack of effective augmentation approach affecting the overall performance of classifiers, which we discuss in this article. Following the analysis of identified articles, we overview the sound datasets, feature extraction methods, data augmentation techniques, and its applications in different areas in the sound classification research problem. Finally, we conclude with the summary of SLR, answers to research questions, and recommendations for the sound classification task.

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Section: Data Augmentation Methods In Sound Classificationmentioning

confidence: 99%

Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

et al. 2022

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“…The most frequently used technique is data aggregation [19], data augmentation [20][21] and data fusion [22]. In data augmentation, the existing data set size is increased by adding more synthetic data to it, or learning from the existing annotated data is done for constructing a more significant size data set; this way, multiple diseases and modalities can be covered.…”

Section: Reviewmentioning

confidence: 99%

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Kaur¹,

Singh²

2022

IJACSA

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For quite some time, the usage of many sources of data (data fusion) and the aggregation of that data have been underappreciated. For the purposes of this study, trials using several medical datasets were conducted, with the results serving as a single aggregated source for identifying eye illnesses. It is proposed in this paper that a diagnostic system that can detect diabetic retinopathy, glaucoma, and cataract can be built as an alternative to current methods. The data fusion and data aggregation techniques used to create this multi-model system made it conceivable. As the name implies, it is a way of compiling data from a large number of legitimate sources. The development of a pipeline of algorithms was accomplished through iterative trials and hyper parameter tweaking. CLAHE (Contrast Level Adaptive Histogram Equalization) approaches, which increase the gradient between picture edges, improve segmentation by raising the contrast between picture edges. The Gabor filter has been shown to be the most effective method of selecting features. The Gabor filter was selected using a hybrid optimization method (LION + Cuckoo), which was developed by the author. For automation, the Support Vector Machine (SVM) radial is the most effective method since it delivers excellent stability and accuracy in terms of accuracy and recall, as well as precision and recall. The discoveries and approaches detailed here provide a more solid foundation for future image-based diagnostics researchers to build on in the future. Eventually, the findings of this study will help to improve healthcare workflows and practices.

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Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

Xin Teoh,

Othmani,

Li Goh

et al. 2024

IEEE Access

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An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions

Cited by 6 publications

References 39 publications

Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

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