An Effective Bone Fracture Detection using Bag-of-Visual-Words with the Features Extracted from SIFT

Rao, Lu; Neelakanteswar, P.; Ramkumar, M.; Krishna, Azmira; Basha, Cmak Zeelan

doi:10.1109/icesc48915.2020.9156035

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…In the comparison experiments, the traditional features are also coded by visual words. As shown in Table 5 , based on the SVM classifier, the average accuracies of LBP-BOW [ 14 ], SURF-BOW [ 15 ], SIFT-BOW [ 13 ], HOG-BOW [ 12 ], and Gabor Wavelets [ 19 ] are 71.36%, 83.36%, 83.84%, 59.04%, and 94.72%, respectively. Moreover, Tables 5 – 7 show that the FLF method and TLF method have more than 10% improvement compared with other methods except Gabor Wavelets.…”

Section: Resultsmentioning

confidence: 99%

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A new method of construction waste classification based on two-level fusion

Lin

Zhao

et al. 2022

PLoS ONE

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The automatic sorting of construction waste (CW) is an essential procedure in the field of CW recycling due to its remarkable efficiency and safety. The classification of CW is the primary task that guides automatic and precise sorting. In our work, a new method of CW classification based on two-level fusion is proposed to promote classification performance. First, statistical histograms are used to obtain global hue information and local oriented gradients, which are called the hue histogram (HH) and histogram of oriented gradients (HOG), respectively. To fuse these visual features, a bag-of-visual-words (BoVW) method is applied to code HOG descriptors in a CW image as a vector, and this process is named B-HOG. Then, based on feature-level fusion, we define a new feature to combine HH and B-HOG, which represent the global and local visual characteristics of an object in a CW image. Furthermore, two base classifiers are used to learn the information from the color feature space and the new feature space. Based on decision-level fusion, we propose a joint decision-making model to combine the decisions from the two base classifiers for the final classification result. Finally, to verify the performance of the proposed method, we collect five types of CW images as the experimental data set and use these images to conduct experiments on three different base classifiers. Moreover, we compare this method with other extant methods. The results demonstrate that our method is effective and feasible.

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Section: Resultsmentioning

confidence: 99%

“…Rao et al . [ 13 ] used the BoVW technique to label the SIFT features extracted from X-ray images as fractured or non-fractured. Local binary patterns (LBP) and the BoVW model were combined for detecting soybean diseases [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A new method of construction waste classification based on two-level fusion

Lin

Zhao

et al. 2022

PLoS ONE

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“…Rao et al [14 ]employed bag-of-visual-words with SIFT-based feature extraction, emphasizing feature engineering's role in accurate detection. Yang & Cheng [15] proposed an artificial neural network-based approach for long-bone fracture detection, showcasing the power of neural networks in medical image analysis.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Empowered Bone Fracture Detection

Baig,

Ul Haq,

Akram

et al. 2024

Decis. Mak. Adv.

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Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.

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“…5 A new visual segment extractions and feature extractions have been developed to classify the fractures in different regions such as the hand, foot, hip, neck, lower leg, and shoulder. 6,7 Different types of bone fractures such as fibula, tibia, ulna, femur, radius, and humerus where are categorized based on its form and the place.…”

Section: Introductionmentioning

confidence: 99%

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

Kumar

Prabu

Pandimurugan

et al. 2022

Concurrency and Computation

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Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

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An Effective Bone Fracture Detection using Bag-of-Visual-Words with the Features Extracted from SIFT

Cited by 13 publications

References 10 publications

A new method of construction waste classification based on two-level fusion

A new method of construction waste classification based on two-level fusion

Transfer Learning Empowered Bone Fracture Detection

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

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