جستجوی مقالات مرتبط با کلیدواژه « deep neural network » در نشریات گروه « پزشکی »

Alzheimer's disease is an irreversible neurological condition characterized by cognitive, behavioral, and memory impairments. Early prediction before the transition from mild cognitive impairment to Alzheimer's disease is still a challenging issue. This study aimed to identify factors associated with Alzheimer's disease.

This study proposes a framework for predicting Alzheimer's disease using data collected from the OASIS project, made available by the Washington University Research Center. In this study, a deep neural network was used for prediction. A particle swarm optimization (PSO) algorithm was employed for selecting appropriate features. The combination of these two methods increases the accuracy of the proposed prediction method.

The results indicate that the proposed method achieves higher accuracy with fewer features. Among the 11 features in this dataset, six features (age, socioeconomic status, Mini-mental state examination score, clinical dementia rating scale, estimated total intracranial volume, and normalized whole-brain volume) have a significant impact on predicting the disease. Among these six features, the clinical dementia rating scale is of great importance.

This study investigated the influential factors and prediction of Alzheimer's disease. Early diagnosis of Alzheimer's disease allows for the provision of appropriate diagnostic and therapeutic services, as well as an improvement in patients' quality of life. The proposed method in this study is compared with various machine learning algorithms that have shown good accuracy in predicting Alzheimer's disease. The results indicate that the accuracy of the proposed method is higher with fewer features.

Today, with the industrialization of societies and the reduction of the size of households and tendency to live alone, caring for the elderly and monitoring their performance in daily life has become doubly important. Carrying devices such a GPS is one of the proposed solutions, which may not be a suitable solution due to the unpleasant feeling of carrying such equipment and due to disorders such as Alzheimer’s disease. A proper solution for taking care of old people especially for their outdoor activities is to observe their behavior by using their mobile GPS sensor by which it is possible to detect possible abnormal events. An important challenge in this method is the high number of abnormal events. In this paper, this problem is solved by applying an adaptive neuro-fuzzy inference system (ANFIS). Other important challenge is how to carefully analyze the training data to achieve a powerful model. For tackling this problem, we used a deep neural network.

In this paper, by combination of ANFIS and convolutional neural networks (CNN), a method was proposed for anomaly detection in trajectory of patients with Alzheimer’s disease. The CNN was optimized by the Whale algorithm. The proposed method was applied on a set of movement path data with a specific origin and destination based on the mobile GPS sensor of subjects. 

The proposed method had an accuracy of 95.5% for classification of test data, which indicated the effectiveness of the proposed method.

It seems that the combination of ANFIS and a CNN is a good method for anomaly detection in trajectory of older people with Alzheimer’s disease.

Cardiovascular diseases are one of the leading causes of mortality in today’s industrial world. Occlusion of left atrial appendage (LAA) using the manufactured devices is a growing trend. The objective of this study was to develop a computer-aided diagnosis system for the identification of LAA in echocardiographic images.

The data used in this descriptive analytical study included 3D echocardiographic images of the heart of 32 patients in King’s College Hospital in London. All patients were treated successfully using the LAA closure device. A total of 208 two-dimensional images were first obtained from each 3D echocardiographic image data set. Then, 1914 images in which the LAA region was clearly recognizable were selected for this study. The proposed neural network was compiled based on the YOLOv3 algorithm. Finally, 1369 and 545 images were used for training and testing the algorithm, respectively.

The performance of the algorithm in detecting the LAA on a set of 545 images was compared with the regions detected in similar images by an expert in echocardiography through intersection over union (IOU). The algorithm was able to correctly identify the LAA region in all 545 examined images with an average IOU of 99.37%.

The proposed image-based algorithm could detect LAA region in echocardiographic images with a high accuracy. This method can be used to develop algorithms for automatic analysis of the LAA region to determine the size of the closure device and to plan an efficient procedure in LAA occlusion methods.

Early diagnosis of Alzheimerchr('39')s disease (AD) seems necessary due to the high cost of care and treatment, the uncertainty of existing therapies, as well as the worrying future of the patient. This study was conducted to AD diagnosis by MRI images using artificial intelligence methods.

In this research, a computer system for early detection of AD with using machine learning algorithms is presented in the framework of computer-aided process. Conditional random field and Inception deep neural network have been adapted for brain MR images to detect AD. Since hippocampal tissue is one of the first tissues to be affected by AD, hence for the early detection of this disease, the hippocampus was located from other brain tissues firstly and then due to the extent to which this tissue is affected, the diagnosis was made. Conditional random field could accurately extract hippocampal fragments of different shapes in all three brain planes. These components serve as the basis for feature extraction by the deep network. The proposed method was tested on standard ADNI dataset images and its performance was demonstrated. The used Inception network has been trained on the huge ImageNet dataset. One of the important steps is knowledge transfer of the problem under consideration. To facilitate this, data augmentation process was applied according to the shape and structure of the hippocampus.

The implemented method in this research, achieved to 98.51% accuracy for two-class classification of "Alzheimer" versus "Normal control" and achieved to 93.41% accuracy for two-class classification of "Mild cognitive impairment" versus "Normal control", which increased by 2.56% and 8.41%, compared with the rival methods, respectively.

The achieved results of this study showed that the using of artificial intelligence techniques has highly accurate in diagnosing AD according to MRI images.

Computer-aided design diagnostic systems are widely used in the differential diagnosis of breast cancer. Therefore, improving the accuracy of a CAD system has become an important field of research. In this paper, we investigated CAD systems based on deep neural networks of convolution type to detect breast cancer in thermographic images.

For analyzing the proposed model, the DMR database has been used. The number of the participants examined were 196, including 41 cases of cancer and 155 healthy subjects. Each person had 10 images of thermography. The total number of the analyzed images included 1960 images of thermography. The classification of thermal images including cancerous and healthy images is based on three types of deep convolution neural networks including google net, resnet18 and vgg16.

The accuracy and specificity of the results using a neural network models of deep pre-training on google-net, resnet18 and vgg16 is 85.03%-89.7%, 83.8% -91.9% and 85.03% -91,01% respectively. The proposed model is capable of providing a significant response to the different breast tissue morphologies.

The model of deep artificial neural network can be used as an efficient and intelligent way to detect cancer in original thermal images without extracting features. However, more studies are needed to design other models of artificial neural networks based on deep learning to detect malignant or benign cancer in thermal imagery.

Skin cancer has grown dramatically over the past decades, and the importance of early treatment is increasing day by day. The purpose of this study is to use deep neural networks to create an auto-diagnosis system for melanoma, in which data is directly controlled as part of a deep learning process. In this paper, studies on related pictures of skin cancer were performed.  For the diagnosis of benign or malignant skin cancer, the deep neural network classifier is used with the help of the Tensorflow framework and the use of the Keras libraries. The dataset which are used in this study consist 70 images of melanoma and 100 images of benign moles. In the proposed model, 80% of the database images are used for training and 20% of the database images are selected for testing. The proposed method offers a higher detection accuracy than other existing methods, which has increased the accuracy of diagnosis in most cases by more than 10%. The high accuracy of the diagnosis and classification and the speed of convergence to the final result are the characteristics of this Research Compared to other Research. An automatic system based on deep learning is presented to identify and categorize skin cancer which provides high accuracy and speed.