Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee, JiHwan; Chung, Seok Won

doi:10.3390/app12020681

Cited by 19 publications

(10 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee and Chung [ 154 ] proposed a Deep Neural Networks (DNNs) classify medical images using transfer learning from image features to enable computer-assisted medical assessment. Contrarian threats are anticipated to be confined even though learning datasets (healthcare illustrations), which are frequently needed for malicious examples, are typically unreachable in contexts of privacy and safety conservation and protection, despite the fact that the adversarial weakness of CNN architectures impedes real world applications due to the elevated shareholdings of prognosis.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

show abstract

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…In the field of orthopedics, ML and DL algorithms have been frequently used in studies such as the detection of fractures [ 7 ], the diagnosis and classification of osteoarthritis [ 8 ], the classification of arthroplasty implants [ 9 , 10 ], and the determination of bone age [ 11 ] on the basis of X-ray images, and very successful results have been achieved. In a recent study conducted by Lee and Chung in 2022 [ 12 ], the DL methods used for orthopedic diseases in medical image analysis were compiled. This study examined the classification of models and manufacturers of knee, hip, and shoulder arthroplasty implants from X-ray images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

et al. 2022

View full text Add to dashboard Cite

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.

show abstract

“…Capturing these specimens into images makes them available for use in computer-aided detection/diagnosis [ 14 ]. Approaches such as machine learning (ML) and, more specifically, deep learning (DL), have been explored in recent years due to their successes in aiding in the prognosis and diagnosis of other medical conditions [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. These techniques provide mathematical models for automating the detection process.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images

Agbley

Hossin

et al. 2022

Diagnostics

View full text Add to dashboard Cite

Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients’ data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature.

show abstract

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Cited by 19 publications

References 77 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images

Contact Info

Product

Resources

About