Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL

Yadav, Dhirendra Prasad; Sharma, Ashish; Athithan, Senthil; Bhola, Abhishek; Sharma, Bhisham; Dhaou, Imed Ben

doi:10.3390/s22155823

Cited by 47 publications

(19 citation statements)

References 43 publications

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“…Mineralized and complex cracks both have many fragments. (14) A bone that has been extensively broken, fractured, or broken into multiple pieces has suffered a mineralized fracture. The shattered bone may penetrate through the skin in a complex fracture.…”

Section: Fracture-type Identificationmentioning

confidence: 99%

“…The shattered bone may penetrate through the skin in a complex fracture. Figure 2 depicts the nature of break in various types of fractures; all long bones have a lengthy diaphyseal portion and proximal and distal regions that are rounded (Figure 1 ) (14) . As a result, both proximal and distal area injuries fall under different subclasses than diaphyseal region fractures.…”

Section: Fracture-type Identificationmentioning

confidence: 99%

“…Fig 1. (a) Various part of tibia, femur, and fibula, (b) Various part of, radius, humours and ulna(14) …”

mentioning

confidence: 99%

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Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

Vironicka¹,

Sathiaseelan²

2023

IJST

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Objectives: Methods in machine learning have been shown to be an essential tool for the diagnosis and treatment of disease. The scientists are constantly looking for new technology that might improve current clinical practise. One of the most well-liked study fields continues to be computerized bone fracture identification and categorization. Furthermore, identifying cracks and determining where they occur have been challenges for historically developed approaches for the long bone fracture method. Methods: We propose a new approach for automatic fracture detection in X-Ray images. This approach is built on top of two-stage fracture detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The described procedure is divided into four main steps: X-ray images are used to: I identify the bone contour; (ii) identify fracture-points or fractures; (iii) find a similar set of shapes that are compatible with the identification of cracks; and (iv) classify and thoroughly evaluate the fracture-type using bounding box. Finding: The resolution process uses the stretched numerical conventional line techniques (RDS), arcs, discrete curvature, and shape directory, among other mathematical characteristics of digital curves. We assessed the suggested model's performance in terms of classification and detection. We divide x-ray pictures of bone fractures into 2 groups, fracture and non-fracture, and we also use a rectangular box to identify the location of fractures. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. Novelty: This study develops a new approach to automatically and accurately detect fractures in X-Ray images. The proposed approach is developed on the basis of Faster R-CNN algorithm with a major modification for the task of rotated bounding box prediction.

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Section: Fracture-type Identificationmentioning

confidence: 99%

Section: Fracture-type Identificationmentioning

confidence: 99%

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Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

Vironicka¹,

Sathiaseelan²

2023

IJST

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“…Zuowei et al [31] defines, an integrated method called RST with ANN used for predicting the damage and this method is enhancement of the MIFS. Many research work included ML techniques for prediction [52], [53], [54], [55].…”

Section: Volume 11 2023mentioning

confidence: 99%

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Chaparala²,

Jain

et al. 2023

IEEE Access

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Standard inspections of buildings are not always possible because of human flaws in prediction. Hence, we need more stable, scalable, and efficient automated processes. Structure Health Monitoring (SHM) is one of the automation systems for forecasting potential losses in building structures. This article suggested how to monitor the strength status of buildings by using Hybrid Machine Learning Technique (HMLT). HMLT contains two-hybrid procedures. One for identifying the most significant features in Dataset using Hybrid Feature Selection Method (HFSM). HFSM uses the combined features of Mutual information (MI) and Rough Set Theory (RST) for feature selection. Another method is optimized classifiers such as Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are used for the classification and predicting the accuracy i.e. predicting the strength status of buildings. Now the proposed method is applied on Earthquake Damage Dataset (Gorkha Earthquake in April 2015). Training and 10-fold crossvalidation procedure pragmatic to features. Then the performance of proposed method has been evaluated using the F1-score and accuracy metrics and get 91% and 92% respectively. Finally, the result analysis demonstrates the importance of the proposed approach in predicting the status of the building strength.

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“…Yadav et al [ 25 ] proposed a highly effective and low-cost hybrid SFNet. Due to the lower computation cost and high efficiency attained by training the model at two scales, the hybrid SFNet is a two-scale DL model with fewer neurons.…”

Section: Introductionmentioning

confidence: 99%

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Ahmed

Mstafa

2022

Diagnostics

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Recently, many diseases have negatively impacted people’s lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people’s quality of life.

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Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL

Cited by 47 publications

References 43 publications

Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

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