Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network

Liu, Yan; She, Guorong; Chen, Shu-xaing

doi:10.1016/j.cmpb.2021.106254

Cited by 30 publications

(18 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ResNet‐18 model won first place in the classification category in the ImageNet competition due to its simplicity and practicality 36,37 . It creates many methods based on ResNet‐50 and ResNet‐101 38 . The ResNet‐18 model consists of 17 convolutional layers and one fully connected layer.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Breast lesion classification using features fusion and selection of ensemble ResNet method

Kilicarslan¹,

Koç

Özyurt

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.

show abstract

Section: Methodsmentioning

confidence: 99%

“…36,37 It creates many methods based on ResNet-50 and ResNet-101. 38 The ResNet-18 model consists of 17 convolutional layers and one fully connected layer. ResNet-101 is a neural network with a depth of 101 layers.…”

Section: Resnet Architecturesmentioning

confidence: 99%

Breast lesion classification using features fusion and selection of ensemble ResNet method

Kilicarslan¹,

Koç

Özyurt

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Meanwhile, a deep learning model for ONFH staging system that uses only MR images has been underexplored. 19 MR-only ONFH staging offers the benefits of reducing costs for imaging and avoiding additional radiation exposure, since it does not require CT images for the diagnosis. Wang et al 20 developed an automatic diagnostic system based on MR images,but its purpose was to segment and detect lesions, not to classify the images into corresponding stages.…”

Section: Introductionmentioning

confidence: 99%

“…Both works performed the staging of ONFH based on radiographs, providing results that are non‐inferior to those of experienced radiologists. Meanwhile, a deep learning model for ONFH staging system that uses only MR images has been underexplored 19 . MR‐only ONFH staging offers the benefits of reducing costs for imaging and avoiding additional radiation exposure, since it does not require CT images for the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Attention fusion network with self‐supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols

2023

View full text Add to dashboard Cite

BackgroundOsteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on Magnetic resonance imaging and computed tomography (CT), which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images.PurposeTo propose a deep learning model for MR‐only ONFH staging, which can reduce additional cost and radiation exposure from the acquisition of CT images.MethodsWe integrated information from the MR images of five different imaging protocols by a newly proposed attention fusion method, which was composed of intra‐modality attention and inter‐modality attention. In addition, a self‐supervised learning was used to learn deep representations from a large amount of paired MR‐CT dataset. The encoder part of the MR‐CT translation network was used as a pretraining network for the staging, which aimed to overcome the lack of annotated data for staging. Ablation studies were performed to investigate the contributions of each proposed method. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the networks.ResultsOur model improved the performance of the four‐way classification of the association research circulation osseous (ARCO) stage using MR images of the multiple protocols by 6.8%p in AUROC over a plain VGG network. Each proposed method increased the performance by 4.7%p (self‐supervised learning) and 2.6%p (attention fusion) in AUROC, which was demonstrated by the ablation experiments.ConclusionsWe have shown the feasibility of the MR‐only ONFH staging by using self‐supervised learning and attention fusion. A large amount of paired MR‐CT data in hospitals can be used to further improve the performance of the staging, and the proposed method has potential to be used in the diagnosis of various diseases that require staging from multiple MR protocols.

show abstract

“…Read More The architecture is then converted into a residual network by adding jump links or residual blocks to this flat network. There are many variants such as ResNet18, ResNet50, and ResNet101 used in the work done in this article[41].…”

mentioning

confidence: 99%

Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

Özyurt¹,

Majidpour

Rashid³

et al. 2023

Preprint

View full text Add to dashboard Cite

Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network

Cited by 30 publications

References 64 publications

Breast lesion classification using features fusion and selection of ensemble ResNet method

Breast lesion classification using features fusion and selection of ensemble ResNet method

Attention fusion network with self‐supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols

Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

Contact Info

Product

Resources

About