KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification Article Swipe
M. J. Aashik Rasool
,
Shabir Ahmad
,
Sabina Umirzakova
,
Taeg Keun Whangbo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3348817
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3348817
Knee osteoporosis (KOP) is a skeletal disorder characterized by bone tissue degradation and low bone density, leading to a high risk of bone fractures in the knee area. The traditional method for identifying knee osteoporosis is knee radiography, which requires sufficient expertise from specialists. However, the sheer volume of X-rays and the subtle variations among them may lead to misinterpretation. In recent years, deep learning algorithms have revolutionized medical diagnosis and reduced misclassification. Specifically, convolutional neural network (CNN)-based algorithms have been utilized to automate the diagnostic process as they have the inherent ability to extract important features that are difficult to identify manually. However, relying on a single method may result in suboptimal performance, leading to ineffective deployment in the medical domain. To alleviate this issue, in this study, we propose a robust detection method, KONet, which utilizes a weighted ensemble approach to distinguish between normal and osteoporotic knee conditions, even when there are minor variations in the data. To validate the architectural choices in the ensemble approach, we conducted experiments on various state-of-the-art CNN-based models using transfer learning. Extensive experiments indicated that the proposed model achieves a higher accuracy than existing models, outperforming the state-of-the-art models by a significant margin.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3348817
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10379081.pdf
- OA Status
- gold
- Cited By
- 16
- References
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- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4390480744Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3348817Digital Object Identifier
- Title
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KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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M. J. Aashik Rasool, Shabir Ahmad, Sabina Umirzakova, Taeg Keun WhangboList of authors in order
- Landing page
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https://doi.org/10.1109/access.2023.3348817Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10379081.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10379081.pdfDirect OA link when available
- Concepts
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Ensemble learning, Computer science, Artificial intelligence, Machine learning, Osteoporosis, Pattern recognition (psychology), Medicine, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 6Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.than | 190 |
| abstract_inverted_index.that | 97, 182 |
| abstract_inverted_index.them | 55 |
| abstract_inverted_index.they | 88 |
| abstract_inverted_index.this | 124, 127 |
| abstract_inverted_index.when | 151 |
| abstract_inverted_index.(KOP) | 2 |
| abstract_inverted_index.among | 54 |
| abstract_inverted_index.area. | 27 |
| abstract_inverted_index.data. | 158 |
| abstract_inverted_index.minor | 154 |
| abstract_inverted_index.model | 185 |
| abstract_inverted_index.sheer | 46 |
| abstract_inverted_index.there | 152 |
| abstract_inverted_index.using | 176 |
| abstract_inverted_index.which | 38, 136 |
| abstract_inverted_index.KONet, | 135 |
| abstract_inverted_index.X-rays | 49 |
| abstract_inverted_index.higher | 188 |
| abstract_inverted_index.issue, | 125 |
| abstract_inverted_index.method | 30, 108 |
| abstract_inverted_index.models | 175, 196 |
| abstract_inverted_index.neural | 75 |
| abstract_inverted_index.normal | 145 |
| abstract_inverted_index.recent | 61 |
| abstract_inverted_index.result | 110 |
| abstract_inverted_index.robust | 132 |
| abstract_inverted_index.single | 107 |
| abstract_inverted_index.study, | 128 |
| abstract_inverted_index.subtle | 52 |
| abstract_inverted_index.tissue | 10 |
| abstract_inverted_index.volume | 47 |
| abstract_inverted_index.years, | 62 |
| abstract_inverted_index.ability | 92 |
| abstract_inverted_index.between | 144 |
| abstract_inverted_index.choices | 163 |
| abstract_inverted_index.domain. | 121 |
| abstract_inverted_index.extract | 94 |
| abstract_inverted_index.leading | 16, 114 |
| abstract_inverted_index.margin. | 200 |
| abstract_inverted_index.medical | 68, 120 |
| abstract_inverted_index.method, | 134 |
| abstract_inverted_index.models, | 192 |
| abstract_inverted_index.network | 76 |
| abstract_inverted_index.process | 86 |
| abstract_inverted_index.propose | 130 |
| abstract_inverted_index.reduced | 71 |
| abstract_inverted_index.relying | 104 |
| abstract_inverted_index.various | 172 |
| abstract_inverted_index.However, | 44, 103 |
| abstract_inverted_index.accuracy | 189 |
| abstract_inverted_index.achieves | 186 |
| abstract_inverted_index.approach | 141 |
| abstract_inverted_index.automate | 83 |
| abstract_inverted_index.density, | 15 |
| abstract_inverted_index.disorder | 6 |
| abstract_inverted_index.ensemble | 140, 166 |
| abstract_inverted_index.existing | 191 |
| abstract_inverted_index.features | 96 |
| abstract_inverted_index.identify | 101 |
| abstract_inverted_index.inherent | 91 |
| abstract_inverted_index.learning | 64 |
| abstract_inverted_index.proposed | 184 |
| abstract_inverted_index.requires | 39 |
| abstract_inverted_index.skeletal | 5 |
| abstract_inverted_index.transfer | 177 |
| abstract_inverted_index.utilized | 81 |
| abstract_inverted_index.utilizes | 137 |
| abstract_inverted_index.validate | 160 |
| abstract_inverted_index.weighted | 139 |
| abstract_inverted_index.CNN-based | 174 |
| abstract_inverted_index.Extensive | 179 |
| abstract_inverted_index.alleviate | 123 |
| abstract_inverted_index.approach, | 167 |
| abstract_inverted_index.conducted | 169 |
| abstract_inverted_index.detection | 133 |
| abstract_inverted_index.diagnosis | 69 |
| abstract_inverted_index.difficult | 99 |
| abstract_inverted_index.expertise | 41 |
| abstract_inverted_index.fractures | 23 |
| abstract_inverted_index.important | 95 |
| abstract_inverted_index.indicated | 181 |
| abstract_inverted_index.learning. | 178 |
| abstract_inverted_index.manually. | 102 |
| abstract_inverted_index.algorithms | 65, 78 |
| abstract_inverted_index.deployment | 117 |
| abstract_inverted_index.diagnostic | 85 |
| abstract_inverted_index.suboptimal | 112 |
| abstract_inverted_index.sufficient | 40 |
| abstract_inverted_index.variations | 53, 155 |
| abstract_inverted_index.(CNN)-based | 77 |
| abstract_inverted_index.conditions, | 149 |
| abstract_inverted_index.degradation | 11 |
| abstract_inverted_index.distinguish | 143 |
| abstract_inverted_index.experiments | 170, 180 |
| abstract_inverted_index.identifying | 32 |
| abstract_inverted_index.ineffective | 116 |
| abstract_inverted_index.significant | 199 |
| abstract_inverted_index.traditional | 29 |
| abstract_inverted_index.osteoporosis | 1, 34 |
| abstract_inverted_index.osteoporotic | 147 |
| abstract_inverted_index.performance, | 113 |
| abstract_inverted_index.radiography, | 37 |
| abstract_inverted_index.specialists. | 43 |
| abstract_inverted_index.Specifically, | 73 |
| abstract_inverted_index.architectural | 162 |
| abstract_inverted_index.characterized | 7 |
| abstract_inverted_index.convolutional | 74 |
| abstract_inverted_index.outperforming | 193 |
| abstract_inverted_index.revolutionized | 67 |
| abstract_inverted_index.state-of-the-art | 173, 195 |
| abstract_inverted_index.misclassification. | 72 |
| abstract_inverted_index.misinterpretation. | 59 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.93961357 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |