Neural Network / Biological & Artificial Neural Network / BNN & ANN
Seyedeh Sadaf Razavinezhad; Amir mohammad Fallah; Seyed Abolghasem Mirroshandel
Volume 14, Issue 4 , February 2021, , Pages 307-320
Abstract
Diabetes is a common disease all around the world. It is a difficult and incurable but controllable disease, so it is important to control and prevent its complications. Thus, low error and smart methods are used to predict blood glucose levels and prevent dangerous complications to control it effectively. ...
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Diabetes is a common disease all around the world. It is a difficult and incurable but controllable disease, so it is important to control and prevent its complications. Thus, low error and smart methods are used to predict blood glucose levels and prevent dangerous complications to control it effectively. In this regard, different methods were investigated. In this research, two models based on deep learning technique are used which produce efficient and optimal results. These models are composed of different combinations of long short-term memory and feed forward neural networks which predict the patient's future blood glucose levels with considerable accuracy and speed. To achieve more comprehensive model, 81,200 blood glucose data was evaluated through 203 patients. In addition, 27 effective features in patients' blood glucose levels are considered in this regard. Furthermore, cross-validation method which is suitable for time series was used for more accurate evaluation. The results showed that Autoregressive Integrated Moving Average model could not predict blood glucose levels considering this amount of data and system hardware limitations, while the models based on deep learning had good performance and good speed. Furthermore, the second proposed model for the prediction horizons of 5, 10, and 15 minutes outperformed the first one with 13.8%, 16%, and 18.9%, respectively. Therefore, the second proposed model can be more reliable for predicting blood glucose. So, it can be used in smart warning systems to prevent hypoglycemia, which is a dangerous and widespread problem of type 1 diabetes.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hamed Abbasi; Shahrokh Shojaei; Nasim Naderi
Volume 13, Issue 2 , August 2019, , Pages 105-115
Abstract
Today, in order to decide on many cardiac surgeries, and whether the patient is able to get under surgery or the time of surgery is passed, it is necessary to measure pulmonary vascular resistance and if the resistance is above a threshold, the patient is considered to be non-surgery; and sometimes, ...
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Today, in order to decide on many cardiac surgeries, and whether the patient is able to get under surgery or the time of surgery is passed, it is necessary to measure pulmonary vascular resistance and if the resistance is above a threshold, the patient is considered to be non-surgery; and sometimes, some therapies are used to reduce the resistance of the pulmonary arteries to the initial disease of the arteries, in which, in order to track down the resistance of the pulmonary vascular, a re-measurement of this parameter is required. Currently, the golden standard of this measure is the use of catheterization procedures, which are aggressive and associated with complications. The purpose of this study is to replace a non-invasive method, rather than an invasive method of cardiac catheterization, by predicting pulmonary vascular resistance based on echocardiographic data by artificial neural networks. Research was performed on 591 patients. Echocardiography was recorded for all subjects, and the echocardiographic data (mPAP, dPAP, sPAP, PCWP, CO) as the neural network input and pulmonary vascular resistance of all patients who were subjected to previous catheterization was evaluated as the output of the neural network and thus, it was obtained, the relationship between echocardiography data and PVRcath. The proposed neural network was typically learned with 75% of the data, and was tested with 25% of the data, and these ratios were modified to better learn the neural network. As a result of implementation, the mean squared error, respectively, for the learning and testing data for the proposed neural network, was 0.37 and 0.27 for the first model, 14.67 and 10.76 for the second model, and 15.82 and 9.58 for the third model.
Biomedical Image Processing / Medical Image Processing
Neda Behzadfar; Hamid Soltanian Zadeh
Volume 7, Issue 3 , June 2013, , Pages 219-236
Abstract
Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many ...
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Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many cases, similarity between tumor and normal tissues. This paper presents semi-automatic approach for analysis of multi-parametric magnetic resonance images (MRI) to segment a highly malignant brain tumor called Glioblastoma multiform (GBM). MRI studies of 12 patients with GBM tumors are used. To show that the proposed method identifies Gd-enhanced tumor pixels from T1-post contrast images minimal user interactions. They are also used to illustrate that the segmentation results obtained by the proposed approach are close to those of an expert, by showing excellent correlations among them (R2=0.97). In order to evaluate the proposed method in practical applications, effects of treatment of GBM brain tumors using Bevacizumab are predicted. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). To this end, two image series of 12 patients before and after treatment and relative changes in the volumes of the Gd-enhanced regions in T1-post contrast images are used as measure of response. The proposed method applies signal decomposition with KNN classifier to minimize user interactions and increase reproducibility of the results. Then histogram analysis is applied to extract statistical features from Gd-enhanced regions of tumor and quantify its micro structural characteristics. Predictive models developed in this work have large regression coefficients (maximum R2=0.91) indicating their capability to predict response to therapy. The results obtained by the proposed approach are compared with those of previous work where excellent correlations are obtained.
Neuro-Muscular Engineering
Mehdi Borjkhani; Farzad Towhidkhah
Volume 4, Issue 2 , June 2010, , Pages 109-122
Abstract
Writing is one of the high practiced and complex movement skills of human. Most of the proposed models for writing are bottom-up models, and therefore they could not reflect the biological aspects of movements in this process. Also there is not any model for illustrating the role of different parts of ...
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Writing is one of the high practiced and complex movement skills of human. Most of the proposed models for writing are bottom-up models, and therefore they could not reflect the biological aspects of movements in this process. Also there is not any model for illustrating the role of different parts of the brain in this task. In this paper we are going to describe some neurological and physiological aspects of the brain operation in the writing task. Then some evidence of prediction in writing and existence of internal models for limbs such as hand are presented. According to these, modeling of writing using model predictive control (MPC) is possible. Based on the presented simulations and experimental results it seems that the modeling of writing by MPC is very similar to the real skill, The proposed model has some advantages such as being consistent with the biological evidence, modeling prediction in writing and high correlation of the statical and dynamical features of the generated letters with those written by human.
Biological Computer Modeling / Biological Computer Simulation
Azade Ahouraei; Farzad Towhidkhah; Fateme Haji Ebrahim Tehrani; Rasoul Khayati
Volume 1, Issue 1 , June 2007, , Pages 63-69
Abstract
Jaundice (hyperbilirubinemia) is a common disease in newborn babies. Under certain circumstances, elevated bilirubin levels may have detrimental neurological effects. In some cases, phototherapy is needed to lower the level of total serum bilirubin, which indicates the presence and severity of jaundice. ...
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Jaundice (hyperbilirubinemia) is a common disease in newborn babies. Under certain circumstances, elevated bilirubin levels may have detrimental neurological effects. In some cases, phototherapy is needed to lower the level of total serum bilirubin, which indicates the presence and severity of jaundice. Recently, diagnosis and treatment modeling of disease have been considered by many researchers. In this paper, we present two models for classification and prediction of neonatal jaundice. The models are based on recorded data of Iranian Neonates. This study is oriented on the basis of following procedures: a short review on physiology of Jaundice, and then description of the models. Two three-layer feed forward neural networks were used in the modeling. The neural network model for classification is able to specify the type of jaundice, and the model for prediction can evaluate the risk of jaundice for newborns. These models can be used to decrease the risk in the critical cases as well as the cost of treatment.
