Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Nadeem, Muhammad Waqas; Goh, Hock Guan; Ali, Abid; Hussain, Muzammil; Khan, Muhammad Adnan; Vasaki, a; Palanisamy, P.

doi:10.3390/diagnostics10100781

Cited by 64 publications

(19 citation statements)

References 89 publications

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“…In future work, recently, the deep learning models [128][129][130][131][132][133][134][135] inspired through images have performed good results. We could use the literature of IoT solutions of supply chain images with the help of convolutional neural networks and other models.…”

Section: Future Work and Limitationsmentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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Through recent progress, the forms of modern supply chains have evolved into complex networks. The supply chain management systems face a variety of challenges. These include lack of visibility of the upstream party (Provider) to the downstream party (Client); lack of flexibility in the face of sudden variations in demand and control of operating costs; lack of reliance on safety stakeholders; ineffective management of supply chain risks. Blockchain (BC) is used in the supply chain to overcome the growing demands for items. The Internet of Things (IoT) is a profoundly encouraging innovation that can help companies observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to answer IoT gadgets in supply chain management. This paper presents orderly writing on and reviewing of Blockchain-based IoT advances and their current usage. We discuss the smart devices used in this system and which device is the most appropriate in the supply chain. This paper also looks at future examination themes in blockchain-based IoT, referred to as the executive’s framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable publications between 2016 and 2021. Lastly, current issues and challenges are present to provide researchers with promising future directions in IoT supply chain management systems.

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Section: Future Work and Limitationsmentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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“…The recent rapid development of deep-learning techniques in ML has inspired increasing use of deep-learning-based image analyses for diagnosing specific diseases [ 19 , 20 ]. convolutional neural network (CNN) constitutes a type of artificial neural network that use convolutional operations [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning

et al. 2021

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Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

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“…A range of Machine Learning (ML) methods such as Logistic Adaptive Networkbased Fuzzy Inference System (LANFIS) [5], Q-learning Fuzzy ARTMAP (FAM), Genetic Algorithm (GA) (QFAM-GA) [6], Hybrid Prediction Model (HPM) [7], Artificial Neural Network (ANN), and Bayesian Networks (BN) (ANN-BN) [8] have been used to develop algorithms for the classification of DB [9,10]. However, reported machine learning-based solutions have been limited in the accuracy of prediction, owing primarily to the lack of the required scope and volume of data for the training and testing of models.…”

Section: Introductionmentioning

confidence: 99%

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

et al. 2021

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A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

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Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Cited by 64 publications

References 89 publications

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

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