scholarly journals The Value of Convolutional-Neural-Network-Algorithm-Based Magnetic Resonance Imaging in the Diagnosis of Sports Knee Osteoarthropathy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rong Yan
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Image Analysis ◽  
Image Data ◽  
Diagnostic Efficiency ◽  
Resonance Imaging ◽  
Analysis Model ◽  
Convolutional Network ◽  
Image Rating ◽  
Complex Relationships

The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images and MRI images of all the research objects were collected after the inclusion of the two groups. Besides, the important physiological representations were extracted from their image data graphs, and the hidden complex relationships were learned. The state without input results was judged through convolutional network calculation, and the result prediction was given. On this basis, there was an analysis of the diagnostic efficiency of traditional DR images and MRI images based on CNN for patients with sports knee osteoarthropathy. The results showed that the MRI images analyzed by the CNN model showed a more obvious display rate than DR images for some nonbone changes of osteoarthritis. The correlation coefficient between MRI image rating and visual analog scale (VAS) was 0.865, which was higher than 0.713 of DR image rating, with a statistical meaning ( P < 0.01 ). For cases with mild lesions, the number of cases detected by MRI based on CNN algorithm in 0–4 image rating was 15, 18, 10, 6, and 7, respectively, which was markedly better than that of DR images. In short, the MRI examination based on the CNN image analysis model could extract important physiological representations from the image data and learn the hidden complex relationships. The convolutional network was calculated to determine the state of the uninput results and give the result predictions. Moreover, MRI examination based on the CNN image analysis model had high overall diagnostic efficiency and grading diagnostic efficiency for patients with motor knee osteoarthropathy, which was of great significance in clinical practice.

scholarly journals Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Deli Wang ◽  
Zheng Gong ◽  
Yanfen Zhang ◽  
Shouxi Wang
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Nasopharyngeal Carcinoma ◽  
Convolutional Neural Network ◽  
Batch Size ◽  
Resonance Imaging ◽  
Dice Coefficient ◽  
Convolutional Network ◽  
Mri Image

The aim of this study was to explore the adoption value of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image intelligent segmentation model in the identification of nasopharyngeal carcinoma (NPC) lesions. The multisequence cross convolutional (MSCC) method was used in the complex convolutional network algorithm to establish the intelligent segmentation model two-dimensional (2D) ResUNet for the MRI image of the NPC lesion. Moreover, a multisequence multidimensional fusion segmentation model (MSCC-MDF) was further established. With 45 patients with NPC as the research objects, the Dice coefficient, Hausdorff distance (HD), and percentage of area difference (PAD) were calculated to evaluate the segmentation effect of MRI lesions. The results showed that the 2D-ResUNet model processed by MSCC had the largest Dice coefficient of 0.792 ± 0.045 for segmenting the tumor lesions of NPC, and it also had the smallest HD and PAD, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively. When batch size = 5, the convergence curve was relatively gentle, and the convergence speed was the best. The largest Dice coefficient of MSCC-MDF model segmenting NPC tumor lesions was 0.896 ± 0.09, and its HD and PAD were the smallest, which were 5.07 ± 0.54 mm and 14.41 ± 1.33%, respectively. Its Dice coefficient was lower than other algorithms ( P < 0.05 ), but HD and PAD were significantly higher than other algorithms ( P < 0.05 ). To sum up, the MSCC-MDF model significantly improved the segmentation performance of MRI lesions in NPC patients, which provided a reference for the diagnosis of NPC.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Quantitative Assessment of Gas Cell Development During the Proofing of Dough by Magnetic Resonance Imaging and Image Analysis

2003 ◽  
Vol 80 (4) ◽  
pp. 390-395 ◽  
Author(s):  
John P. M. van Duynhoven ◽  
Geert M. P. van Kempen ◽  
Robert van Sluis ◽  
Bernd Rieger ◽  
Peter Weegels ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Analysis ◽  
Magnetic Resonance ◽  
Quantitative Assessment ◽  
Cell Development ◽  
Resonance Imaging ◽  
Gas Cell

scholarly journals A New Retrieval Algorithm Based on Pulse Coupled Neural Network for Biomedical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 9-13
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rapid Expansion ◽  
Image Feature ◽  
Experimental Result ◽  
Feature Descriptor ◽  
Resonance Imaging ◽  
Biomedical Images ◽  
Pulse Coupled Neural Network

The rapid expansion and improvement in medical science and technology lead to the generation of more image data in its regular activity such as computed tomography (CT), X-ray, magnetic resonance imaging (MRI) etc. To manage the medical images properly for clinical decision making, content-based medical image retrieval (CBMIR) system emerged. In this paper, Pulse Coupled Neural Network (PCNN) based feature descriptor is proposed for retrieval of biomedical images. Time series is used as an image feature which contains the entire information of the feature, based on which the similar biomedical images are retrieved in our work. Here, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. Open Access Series of Imaging Studies (OASIS) magnetic resonance imaging dataset is used for the evaluation of the proposed approach. The experimental result of the proposed system shows that the retrieval efficiency is better than the other existing systems.

scholarly journals THE ROLE OF MAGNETIC RESONANCE IMAGING IN THE DIAGNOSIS OF DEFORMING ARTHROSIS OF PROFESSIONAL ETIOLOGY IN MINERS

2018 ◽  
Vol 5 ◽  
pp. 3-11
Author(s):  
Angela Basanets ◽  
Maria Bulavko
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Knee Joint ◽  
Joint Damage ◽  
Knee Joints ◽  
Prognostic Models ◽  
Diagnostic Efficiency ◽  
Resonance Imaging ◽  
Physical Loading ◽  
Medial Hypertrophy

The paper analyzes the effectiveness of magnetic resonance imaging with cartilage diagram in diagnosing signs of professional deforming arthrosis of knee joints in miners working in conditions of significant physical loading. Aim of the research – to determine of diagnostic efficiency of indicators of magnetic resonance imaging of the knee joint and cartilage diagram in miners of the main occupations suffering from deforming arthrosis. Methods. The research is conducted in 30 miners of basic occupations: 20 mining workers of breakage face (MWBF) and 10 machinists of shearer mining machines (МSMM) have been treated in the inpatient department of occupational pathology of the Lviv Regional Clinical Hospital in 2015-2017 due to deforming arthrosis. Damages of the main anatomical elements of the knee joint with arthrosis were analyzed, visualized initially with the help of MRI, and then - cartilage diagram. Results. According to the MRI data, in miners of the main occupations with arthrosis of the knee joint the posterior cross-shaped ligament are most commonly affected (in 75.0±9.7 % MWBF and 70.0±14.5 % МSMM), damage to the medial collateral ligament are diagnosed less frequently (in 5.0±4.9 % in the MWBF and in 10.0±9.5 % in the МSMM). On average 3.8±0.4 modified elements of the knee joint are visualized in patients, whereas 4.8±0.1 affected areas are visualized on the cartilage diagram (р<0.05). In 86.7±6.2 % patients, in the analysis of cartilage diagram, changes in all five analyzed areas are diagnosed, indicating a higher efficiency of the diagnosis of changes in the structures of the joint with DA of the professional etiology of the method of cartilage diagram compared with MRI. According to the cartilage diagram the most significant changes are noted in the hypertrophy of the femur: among all miners 62.5±0.3 ms (medial) and 62.6±0.4 ms (lateral), in the MWBF group the average time of Т2-delay is the largest in the area of the medial hypertrophy of the femur is 60.9±2.3 ms, in the МSMM group – in the area of the lateral hypertrophy of the femur: 66.7±3.3 ms, which can be linked to the peculiarities of the forced working position of miners of these professions and the kinetics of joint structures. These results can be used to diagnose the initial lesions of joint structures with DA of professional genesis, as well as the creation of prognostic models for determining the the degree of risk of development of knee joint damage, which will allow to improve the system of personified approach to diagnostic and preventive measures in working persons in conditions of considerable physical activity and forced working position.

scholarly journals Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging)

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Ida Bagus Leo Mahadya Suta ◽  
Rukmi Sari Hartati ◽  
Yoga Divayana
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Tumor otak menjadi salah satu penyakit yang paling mematikan, salah satu jenis yang paling banyak ditemukan adalah glioma sekitar 6 dari 100.000 pasien adalah penderita glioma. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam menganalisa dan mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki resiko kesalahan yang tinggi, untuk itu dibutuhkan suatu cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Convolutional Neural Network (CNN) menjadi salah satu solusi dalam melakukan klasifikasi otomatis dalam citra MRI. CNN merupakan algoritma deep learning yang memiliki kemampuan untuk belajar sendiri dari kasus kasus sebelumnya. Dan dari penelitian yang telah dilakukan, diperoleh hasil bahwa CNN mampu dalam menyelesaikan klasifikasi tumor otak dengan akurasi yang tinggi. Peningkatan akurasi diperoleh dengan mengembangkan algoritma CNN baik melalui menentukan nilai kernel dan/atau fungsi aktivasi.

scholarly journals Sonographic Pearls for Imaging the Brachial Plexus and Its Pathologies

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 324
Author(s):  
Po-Cheng Hsu ◽  
Ke-Vin Chang ◽  
Kamal Mezian ◽  
Ondřej Naňka ◽  
Wei-Ting Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
High Resolution ◽  
Brachial Plexus ◽  
Inflammatory Diseases ◽  
Technical Skills ◽  
Resonance Imaging ◽  
Sonographic Examination ◽  
Diagnostic Modality ◽  
Different Parts

The brachial plexus (BP) is a complicated neural network, which may be affected by trauma, irradiation, neoplasm, infection, and autoimmune inflammatory diseases. Magnetic Resonance Imaging is the preferred diagnostic modality; however, it has the limitations of high cost and lack of portability. High-resolution ultrasound has recently emerged as an unparalleled diagnostic tool for diagnosing postganglionic lesions of the BP. Existing literature describes the technical skills needed for prompt ultrasound imaging and guided injections for the BP. However, it remains particularly challenging for beginners to navigate easily while scanning its different parts. To address this, we share several “clinical pearls” for the sonographic examination of the BP as well as its common pathologies.

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