scholarly journals Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Bo ◽  
Junli Xie ◽  
Jianguo Zhou ◽  
Shikun Li ◽  
Yuezhan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Convolutional Neural Network ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Gray Level ◽  
Resonance Imaging

The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolutional neural network (CNN). Fifty patients with cerebral infarction were enrolled and examined by MRI. Besides, a CNN artificial intelligence system was established for learning and training. The features were extracted from the MRI image results of the patients, and then, the data were calculated by computer technology. The gray-level cooccurrence matrix (GLCM) of T1-weighted images was 0.872 ± 0.069; the reasonable prediction (ALL) result was 0.766 ± 0.112; the gray-level run-length matrix (GLRLM) was 0.812 ± 0.101; the multigray-level area size matrix (MGLSZM) result was 0.713 ± 0.104; and the result of gray-scale area size matrix (GLSZM) was 0.598 ± 0.099. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of enhanced T1-weighted images were 0.710 ± 0.169, 0.742 ± 0.099, 0.778 ± 0.096, 0.801 ± 0.104, and 0.598 ± 0.099, respectively. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of T2-weighted images were 0.780 ± 0.096, 0.798 ± 0.087, 0.888 ± 0.086, 0.768 ± 0.112, and 0.767 ± 0.100, respectively. In short, the image diagnosis method that could reduce the subjective visual judgment error to a certain extent was found by analyzing the characteristics of MRI images of critically ill patients with cerebral infarction based on CNN.

scholarly journals Magnetic Resonance Imaging Image Segmentation Under Artificial Intelligence Neural Network for Evaluation of the Effect of Butyphthalide Combined With Edaravone on Neurological Function in Patients With Acute Cerebral Infarction

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Li ◽  
Guoping Liu
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Acute Cerebral Infarction ◽  
Resonance Imaging ◽  
Vascular Stenosis ◽  
Mri Image

This research was developed to investigate the effect of artificial intelligence neural network-based magnetic resonance imaging (MRI) image segmentation on the neurological function of patients with acute cerebral infarction treated with butylphthalide combined with edaravone. Eighty patients with acute cerebral infarction were selected as the research subjects, and the MRI images of patients with acute cerebral infarction were segmented by convolutional neural networks (CNN) upgraded algorithm model. MRI images of patients before and after treatment of butylphthalide combined with edaravone were compared to comprehensively evaluate the efficacy of this treatment. The results showed that compared with the traditional CNN algorithm, the running time of the CNN upgraded algorithm adopted in this study was significantly shorter, and the Loss value was lower than that of the traditional CNN model. Upgraded CNN model can realize accurate segmentation of cerebral infarction lesions in MRI images of patients. In addition, the degree of cerebral infarction and the degree of arterial stenosis were significantly improved after treatment with butylphthalide and edaravone. Compared with that before treatment, the number of patients with severe cerebral infarction or even vascular stenosis decreased significantly (P < 0.05), and gradually changed to mild vascular stenosis, and the neurological dysfunction of patients was also significantly improved. In short, MRI image segmentation based on artificial intelligence neural network can well-evaluate the efficacy and neurological impairment of butylphthalide combined with edaravone in the treatment of acute cerebral infarction, and it was worthy of promotion in clinical evaluation of the treatment effect of acute cerebral infarction.

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 Diffusion-Weighted Imaging-Magnetic Resonance Imaging Information under Class-Structured Deep Convolutional Neural Network Algorithm in the Prognostic Chemotherapy of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yong Hu ◽  
Jie Tang ◽  
Shenghao Zhao ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Neoadjuvant Chemotherapy ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Imaging ◽  
Resonance Imaging ◽  
Imaging Diagnosis ◽  
The Difference ◽  
Mri Image

In order to improve the efficiency of early imaging diagnosis of patients with osteosarcoma and the effect of neoadjuvant chemotherapy based on the results of imaging examinations, 48 patients with suspected osteosarcoma were selected as the research objects and their diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI) images were regularized in this study. Then, a DWI-MRI image discrimination model was established based on the class-structured deep convolutional neural network (CSDCNN) algorithm. The peak signal-to-noise ratio (PSNR), mean square error (MSE), and edge preserve index (EPI) were applied to evaluate the image quality after processing by the CSDCNN algorithm; the accuracy, recall rate, precise rate, and F1 score were employed to evaluate the diagnostic efficiency of CSDCNN algorithm; the apparent diffusion coefficient (ADC) was adopted to evaluate the therapeutic effect of neoadjuvant chemotherapy based on the CSDCNN algorithm, and SegNet, LeNet, and AlexNet algorithms were introduced for comparison. The results showed that the PSNR, MSE, and EPI values of DWI-MRI images of patients with osteosarcoma were 29.1941, 0.0016, and 0.9688, respectively, after using the CSDCNN algorithm to process the DWI-MRI images. The three indicators were significantly better than other algorithms, and the difference was statistically significant ( P < 0.05 ). According to the results of imaging diagnosis of patients with osteosarcoma, there was no significant difference between the assisted diagnosis effect of the CSDCNN algorithm and the pathological examination results ( P > 0.05 ). The results of adjuvant chemotherapy based on the CSDCNN algorithm found that the ADCmean value of the patients after chemotherapy was 1.66 ± 0.17 and the ADCmin value was 1.33 ± 0.15; the two indicators were significantly higher than other algorithms, and the difference was statistically significant ( P < 0.05 ). In conclusion, the CSDCNN algorithm had a good effect on DWI-MRI image processing of patients with osteosarcoma, which could improve the diagnostic accuracy of patients with osteosarcoma. Moreover, the diagnosis results based on this algorithm could achieve better neoadjuvant chemotherapy effects and assist clinicians in imaging diagnosis and clinical treatment of patients with osteosarcoma.

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