scholarly journals Deep Learning based Multi‐modal Computing with Feature Disentanglement for MRI Image Synthesis

2021 ◽  
Author(s):  
Yuchen Fei ◽  
Bo Zhan ◽  
Mei Hong ◽  
Xi Wu ◽  
Jiliu Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Synthesis ◽  
Mri Image

A Novel Deep Learning Based Auto Segmentation For Rectum Tumor On MRI Image

2018 ◽  
Vol 102 (3) ◽  
pp. e548
Author(s):  
J. Wang ◽  
J. Lu ◽  
G. Qing ◽  
L. Shen ◽  
Y. Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mri Image

Brain Tumor Detection using Hybrid Deep Learning with Genetic Algorithm

2021 ◽  
Vol 23 (09) ◽  
pp. 981-993
Author(s):  
T. Balamurugan ◽  
◽  
E. Gnanamanoharan ◽  
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Brain Tumor ◽  
Advanced Technology ◽  
Great Promise ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Feature Subset ◽  
Brain Tumor Segmentation ◽  
Mri Image

Brain tumor segmentation is a challenging task in the medical diagnosis. The primary aim of brain tumor segmentation is to produce precise characterizations of brain tumor areas using adequately placed masks. Deep learning techniques have shown great promise in recent years for solving various computer vision problems such as object detection, image classification, and semantic segmentation. Numerous deep learning-based approaches have been implemented to achieve excellent system performance in brain tumor segmentation. This article aims to comprehensively study the recently developed brain tumor segmentation technology based on deep learning in light of the most advanced technology and its performance. A genetic algorithm based on fuzzy C-means (FCM-GA) was used in this study to segment tumor regions from brain images. The input image is scaled to 256×256 during the preprocessing stage. FCM-GA segmented a preprocessed MRI image. This is a versatile advanced machine learning (ML) technique for locating objects in large datasets. The segmented image is then subjected to hybrid feature extraction (HFE) to improve the feature subset. To obtain the best feature value, Kernel Nearest Neighbor with a genetic algorithm (KNN-GA) is used in the feature selection process. The best feature value is fed into the RESNET classifier, which divides the MRI image into meningioma, glioma, and pituitary gland regions. Real-time data sets are used to validate the performance of the proposed hybrid method. The proposed method improves average classification accuracy by 7.99 % to existing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) classification algorithms

Medical Sports Rehabilitation Deep Learning System of Sports Injury Based on MRI Image Analysis

2020 ◽  
Vol 10 (5) ◽  
pp. 1091-1097 ◽  
Author(s):  
Hongbing Ba
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Circulatory System ◽  
Sports Injury ◽  
Learning System ◽  
Experimental Result ◽  
Static State ◽  
Prevention Campaign ◽  
The Central Nervous System ◽  
Mri Image

Medical sports rehabilitation deep learning system of sports injury based on MRI image analysis is proposed in this paper. Preparation activities are various body exercises that are purposely performed before physical education, training, and competition. It is a transitional phase from the static state to the moving state of the human body. Preparatory activities can improve the excitability of the central nervous system, improve the ability of the cerebral cortex to analyze and judge movements, and thus make the movement more coordinated and accurate. At the same time prepare activity can also improve the respiratory and circulatory system functions and reduce the muscles, ligaments of the sticky nature and the contraction of muscles for speed and strength, in order to maximize the capacity of the physical movement and injury prevention campaign ready. Therefore, how to use the MRI image to numerically analyze the mentioned task is essential. We integrate the deep learning model to propose the novel image enhancement and recognition model to undertake the task of medical sports rehabilitation system. The experimental result proves the performance is robust.

scholarly journals Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zijian Li ◽  
Shiyou Ren ◽  
Xintao Zhang ◽  
Lu Bai ◽  
Changqing Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Joint ◽  
Knee Function ◽  
Partial Resection ◽  
Knee Meniscus ◽  
Meniscus Injury ◽  
Secondary Resection ◽  
Network Algorithm ◽  
Meniscus Suture ◽  
Mri Image

The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were compared between SRCNN and other algorithms. Sixty patients with meniscus injury of knee joint were studied. Arthroscopic surgery was performed according to the patients’ actual type of injury, and knee scores were evaluated for all patients. Then, postoperative scores and MRI results were analyzed. The results showed that the PSNR and SSIM values of the SRCNN algorithm were (42.19 ± 4.37) dB and 0.9951, respectively, which were significantly higher than those of other algorithms ( P  < 0.05). Among patients with meniscus injury, 17 cases (28.33%) were treated with meniscus suture, 39 cases (65.00%) underwent secondary resection, 3 cases (5.00%) underwent partial resection, and 1 case (1.67%) underwent full resection. After meniscus suture, secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. There was no great difference in knee function scores after different methods of treatment ( P  > 0.05), and there were considerable differences compared with those before treatment ( P  < 0.01). Compared with the results of arthroscopy, there was no significant difference in the grading of meniscus injury by MRI ( P  > 0.05). To sum up, the SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Arthroscopic knee surgery had good results and had great clinical application and promotion value.

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

scholarly journals The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1055
Author(s):  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Jun Oyama ◽  
Emi Yamaga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Clinical Practice ◽  
Data Extraction ◽  
Ultrasonic Imaging ◽  
Image Synthesis ◽  
Breast Ultrasound ◽  
Learning Technology ◽  
Patient Prognosis ◽  
Improve Patient

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

scholarly journals Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yafen Li ◽  
Wen Li ◽  
Jing Xiong ◽  
Jun Xia ◽  
Yaoqin Xie
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Medical Image ◽  
Image Synthesis ◽  
Ct Images ◽  
Learning Method ◽  
Learning Methods ◽  
Unsupervised Deep Learning ◽  
Net Method ◽  
Synthetic Images

Cross-modality medical image synthesis between magnetic resonance (MR) images and computed tomography (CT) images has attracted increasing attention in many medical imaging area. Many deep learning methods have been used to generate pseudo-MR/CT images from counterpart modality images. In this study, we used U-Net and Cycle-Consistent Adversarial Networks (CycleGAN), which were typical networks of supervised and unsupervised deep learning methods, respectively, to transform MR/CT images to their counterpart modality. Experimental results show that synthetic images predicted by the proposed U-Net method got lower mean absolute error (MAE), higher structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) in both directions of CT/MR synthesis, especially in synthetic CT image generation. Though synthetic images by the U-Net method has less contrast information than those by the CycleGAN method, the pixel value profile tendency of the synthetic images by the U-Net method is closer to the ground truth images. This work demonstrated that supervised deep learning method outperforms unsupervised deep learning method in accuracy for medical tasks of MR/CT synthesis.

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