scholarly journals Brain tissue development of neonates with Congenital Septal Defect: Study on MRI Image Evaluation of Deep Learning Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Jianfei Zhu ◽  
Jiaolei Chen ◽  
Yunhui Zhang ◽  
Jianwei Ji
Keyword(s):  
Deep Learning ◽  
Brain Tissue ◽  
Congenital Heart ◽  
Septal Defect ◽  
Learning Algorithm ◽  
Neonatal Brain ◽  
Septal Defects ◽  
Deep Learning Algorithm ◽  
C Value ◽  
The Difference

Objectives: This article is based on deep learning algorithms and uses MRI to study the development of congenital heart septal defects in neonatal brain tissue. Methods: From January 2018 to December 2019, 150 cases of congenital cardiac paper septal defect were retrospectively analyzed on 50 cases of normal newborns and neonates. The four index parametersbrain MR imaging, lateral ventricle pre-angle measurement index (F/F’), body index (D/ D’), caudal nucleus index (C/C’) were analyzed. The independent sample t test is performed to compare the difference parameters between groups. Results: F congenital heart disease group and control group/F ‘values were 0.301 ± 0.035 and 0.296 ± 0.031; Evans index was 0.239 ± 0.052 and 0.233 ± 0.025; 2 sets of D/D’ values were 0.261 ± 0.039 and 0.234 ± 0.032; C/C ‘value was 0.138 ± 0.018 and 0.124 ± 0.015 respectively. The congenital heart disease group D/D ‘, and the value of C/C’ obtained under the ROC curve area value, respectively 0.698 and 0.750, Youden index corresponding to the maximum D/D ‘, and the value of C/C’ values were 0.28 and 0.12. Conclusion: Lateral ventricle D/D ‘and C/C’ is more sensitive indicator which can be evaluated with the difference between the volume of congenital heart septal defects in newborn normal neonatal brain; when the D/D ‘value> 0.28, C/C’ value> 0.12. For the diagnosis and evaluation of congenital heart septal defect neonatal brain volume abnormalities have a certain reference value. List of acronyms: MRI: Magnetic Resonance Imaging. POX: Pulse oximetry. CHD: Congenital Heart Disease.DWI: Diffusion-weighted Imaging. T1WI: T1-weighted imaging T2WITSE: T2-weighted imaging, Turbo Spin Echo. FOV: Field of View. FLAIR: Fluid Attenuated Inversion Recovery. TE: Echo Time. TR: Repetition Time. ICC: Intra-group Correlation Coefficient. doi: https://doi.org/10.12669/pjms.37.6-WIT.4863 How to cite this:Zhu J, Chen J, Zhang Y, Ji J. Brain tissue development of neonates with Congenital Septal Defect: Study on MRI Image Evaluation of Deep Learning Algorithm. Pak J Med Sci. 2021;37(6):1652-1656. doi: https://doi.org/10.12669/pjms.37.6-WIT.4863 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2021 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2020 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Biopsy Tissue ◽  
The Difference

Abstract Background：Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue.Methods：We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition.Results: The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms.Conclusion：IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.

scholarly journals Measuring accuracy of Dataset using Deep Learning Algorithm RMSProp Algorithm

2021 ◽  
Vol 9 (10) ◽  
pp. 99-112
Author(s):  
Sanjana Naidu Gedela
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Medical Centers ◽  
Clinical Methods ◽  
Costs Of Treatment ◽  
The Difference ◽  
Hidden Layer ◽  
Data Elements

Abstract: Over the last few years, there have been many significant improvements in the field of AI, machine learning, deep learning are being used in various industries and research. In order to train the deep learning models learning of parameters plays a major role, here the reduction of loss incurred during the training process is the main objective. In a supervised mode of learning, a model is given the data samples and their respective outcomes. When a model generates an output, it compares it with the desired output and then takes the difference of generated and desired outputs and then attempts to bring the generated output close to the desired output. This is achieved through optimization algorithms. Though many kinds of clinical methods have been employed to detect whether patients have heart disease or not by number of features from patients. but it’s still a challenging task due to the multitude of data elements involved. The motive of our project is to save human resources in medical centers and improve accuracy of diagnosis. In our project we used an RMS prop optimizer. The purpose is to decide how many hidden layers need to be selected and how many neurons need to be selected in each and every hidden layer by looking at the dataset and to give the application of deep learning to the health care sector so that we can minimize the costs of treatment and help in proactive actions. We want to show that we can increase the accuracy of the project by taking stability along with accuracy into consideration. Index Terms: RMS Prop, Machine Learning, Deep Learning, number of features, proactive actions

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2020 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Biopsy Tissue ◽  
The Difference

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue. Methods We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.

scholarly journals Classification of Retinal Image for Early Detection of Diabetic Retinopathy Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1552-1555
Author(s):  
Piyush Kumar
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Learning Algorithm ◽  
Retinal Images ◽  
Data Sets ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Learning Machine ◽  
Deep Learning Network

Abstract: Diabetic retinopathy is a disease which cause of blindness due to diabetes. For this reason, it is very important to detect diabetic retinopathy in early stages. A deep learning-based approach is used for the early detection of diabetic retinopathy from retinal images. The proposed approach consists of two steps. In the first stage, pre treatments were performed to remove retinal images from different data sets and standardize them to size. In the second stage, classification is done by the help of Convolutional Neural Network using deep learning algorithm and 98.5% success is achieved. The difference of this technique from similar studies is that instead of creating the feature set manually as in traditional methods, the deep learning network automatically constructs and trained by using CPU and GPU in a very short time. Keywords: CNN, Early detection, Artificial intelligence, Deep learning, Machine-learning, Fundus Image, Optical coherence tomography, Ophthalmology.

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