Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)

Ölmez, Emre; Akdoğan, Volkan; Korkmaz, Murat; Er, Orhan

doi:10.1007/s10278-020-00329-x

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…OA reduces mobility, quality of life, and productivity while increasing morbidity, healthcare use, and social expenditure [1]. OA creates a significant individual and societal burden [22].…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model

Aslan¹

2023

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Knee osteoarthritis (Knee-OA) is one of the most common musculoskeletal diseases caused by loss of cartilage and bone changes in the joint. Prediction of early Knee-OA based on early bone tissue analysis is challenging in medical image analysis. If the disease is detected in the later stages, it may cause serious problems, such as the need for knee replacement. Therefore, the detection of Knee-OA disease is essential. With the developing technology, computer-aided systems have been frequently used in the biomedical field in recent years. A deep learning-based hybrid model for the early diagnosis and treatment of Knee-OA disease was developed in this study. In the developed hybrid model, three different CNN architectures were used as the base, and feature extraction was made with these architectures. The features obtained in three different architectures are combined to bring together different features of the same image. After merging, the neighboring component analysis (NCA) size reduction method was used to remove unnecessary features. Since unnecessary features are eliminated from the feature map optimized with NCA, the proposed hybrid model will work faster and produce more successful results. Finally, the feature map optimized with NCA was classified with six different classifiers. The proposed model was also compared to eight different CNN architectures. In comparison to CNN architectures, the proposed hybrid model achieved the highest accuracy performance.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model

Aslan¹

2023

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“…Recently, brain tumour classification of MR images has been addressed widely with transfer learning from models trained with natural images, such as AlexNet [7,21,22], VGG16 [23], VGG19 [24], ResNet34 [25], ResNet 101 [22], ResNet 50 [22], GoogLeNet [22], and SqueezeNet [22]. Transfer learning from natural images to accomplish medical images tasks has also been tested on MR images enhancement [26], cine MR images super-resolution [27], and detection of meniscus region on MR images [28].…”

Section: Related Workmentioning

confidence: 99%

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

Mecheter,

Abbod,

Zaidi

et al. 2023

CAAI Trans on Intel Tech

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Magnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1‐weighted (T1‐w) to T2‐weighted (T2‐w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation‐based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi‐parametric segmentation model is proposed using T2‐w MR as an intensity‐based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2‐w‐based augmentation has no significant impact on the performance of T1‐w MR segmentation, it helps in improving T2‐w MR segmentation and developing a multi‐sequences segmentation model.

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“…CNN is among the most popular deep learning algorithms and it learns to perform the classification process directly from image, video, text, or audio files. CNN is quite similar to ordinary ANNs, and it consists of neurons with learnable weight and bias values just like ordinary ANNs [24][25][26][27]. The biggest difference of CNN from ordinary ANNs is that by nature it assumes its inputs as two-or three-dimensional images.…”

Section: Covid-19 Detection Using Deep Neural Network Algorithmmentioning

confidence: 99%

A Comparative Study on COVID-19 Prediction Using Deep Learning and Machine Learning Algorithms: A Case Study on Performance Analysis

Arslan

2022

Sakarya University Journal of Computer and Information Sciences

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COVID-19 disease has been the most important disease recently and has affected serious number of people in the world. There is not proven treatment method yet and early diagnosis of COVID-19 is crucial to prevent spread of the disease. Laboratory data can be easily accessed in about 15 minutes, and cheaper than the cost of other COVID-19 detection methods such as CT imaging and RT-PCR test. In this study, we perform a comparative study for COVID-19 prediction using machine learning and deep learning algorithms from laboratory findings. For this purpose, nine different machine learning algorithms including different structures as well as deep neural network classifier are evaluated and compared. Experimental results conduct that cosine k-nearest neighbor classifier achieves better accuracy with 89% among other machine learning algorithms. Furthermore, deep neural network classifier achieves an accuracy of 90.3% when one hidden layer including 60 neurons is used to detect COVID-19 disease from laboratory findings data.

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Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)

Cited by 19 publications

References 18 publications

Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model

Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

A Comparative Study on COVID-19 Prediction Using Deep Learning and Machine Learning Algorithms: A Case Study on Performance Analysis

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