Showing 109–120 of 180 results
Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly PatientsOpen Access
Abstract: For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.
Supervised Learning-based Nuclei Segmentation on Cytology Pleural Effusion Images with Artificial Neural NetworkOpen Access
Abstract: Automated segmentation of cell nuclei is the crucial step towards computer-aided diagnosis system because the morphological features of the cell nuclei are highly associated with the cell abnormality and disease. This paper contributes four main stages required for automatic segmentation of the cell nuclei on cytology pleural effusion images. Initially, the image is preprocessed to enhance the image quality by applying contrast limited adaptive histogram equalization (CLAHE). The segmentation process is relied on a supervised Artificial Neural network (ANN) based pixel classification. Then, the boundaries of the extracted cell nuclei regions are refined by utilizing the morphological operation. Finally, the overlapped or touched nuclei are identified and split by using the marker-controlled watershed method. The proposed method is evaluated with the local dataset containing 35 cytology pleural effusion images. It achieves the performance of 0.95%, 0.86 %, 0.90% and 92% in precision, recall, F-measure and Dice Similarity Coefficient respectively. The average computational time for the entire algorithm took 15 mins per image. To our knowledge, this is the first attempt that utilizes ANN as the segmentation on cytology pleural effusion images.
A Medical Training System Using Augmented Reality: Trial Development of Environment PlatformOpen Access
Abstract: A medical training system using augmented reality, AR is presented in this paper. Recognizing an AR marker through a Web camera, computer generated images appear on a real place. We prepared 3D (three-dimensional) anatomical objects to show, and evaluated our system using AR platform. It was found from the experimental result that our system overlaid the digital information of the 3D anatomical objects on the operator’s surrounding real world appropriately, and that feature of visible/invisible objects was verified in AR environment platform.
Making protein-protein interaction prediction more reliable with a large-scale dataset at the proteome levelOpen Access
Abstract: Reliable information about protein-protein interactions (PPIs) enables us to better understand biological processes, pathways and functions. However, there are many experimental problems in identifying complete PPI-networks in a cell or organism. To supplement the limitations of current experimental techniques, we have previously proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) . In the PPI prediction, the selection of datasets is a big issue for accurately evaluating the performance of different algorithms [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high-confidence direct physical PPIs obtained from TargetMine  (as PPIs being only 0.13% of the total) and 33,098,951 non-PPIs. As a result, the new prediction model achieved a higher AUC of 0.89 (pAUCFPR≤0.5% = 0.24) than the previous model of PSOPIA. Furthermore, it was applied to the problem of filtering out protein pairs incorrectly labeled as interacting from a low-confidence human PPI dataset. Here, we suggest that a diverse set of large-scale examples is key to more reliable PPI prediction, demonstrating the performance of PSOPIA at the proteome level.
Automatic Diabetic Retinopathy Screening Based on Morphological Operation and Support Vector Machine IntegrationOpen Access
Abstract: Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.
Accuracy verification of knife tip positioning with position and orientation estimation of the actual liver for liver surgery support systemOpen Access
Abstract: We are developing a surgical support system for liver abdominal surgery to prevent surgical accidents. Our support system can detect proximity of certain body parts and can make a warning when the knife approaches the critical part to be excised, such as large blood vessels. The system has two distance cameras with different features, and these cameras are located above the operating table. One camera measures the liver shape and the other tracks the position of a surgical knife during surgery. In this report, to evaluate the accuracy of the distance between the position of the knife tip and the position of the blood vessels, the position and orientation of the liver were estimated using depth images of mock liver and virtual liver by simulated annealing algorithm, and the distance between the knife tip and the blood vessel in the mock liver was measured. The experimental results showed the maximum average error of the measured distance was 5.76 mm.
Deep Learning based Handwritten Digit RecognitionOpen Access
Abstract: Neural network and depth learning have been widely used in the field of image processing. Good recognition results are often required for complex network models. But the complex network model makes training difficult and takes a long time. In order to obtain a higher recognition rate with a simple model, the BP neural network and the convolutional neural network are studied separately and verified on the MNIST data set. In order to improve the recognition results further, a combined depth network is proposed and validated on the MNIST dataset. The experimental results show that the recognition effect of the combined depth network is obviously better than that of a single network. A more accurate recognition result is achieved by the combined network.
Water Bloom Warning Model Based on Random ForestOpen Access
Abstract: Based on the random forest classification algorithm, a warning model of water bloom is proposed. Using the collected data, Select the water quality, meteorological factors which like Chlorophyll a (Chl-a), water temperature (T), PH, nitrogen and phosphorus ratio (TN:TP), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), dissolved oxygen Light (E) and so on as the impact factor and use them establish a warning model for Water bloom. And compared with the prediction accuracy of neural network model and SVM model. The results show that the water bloom warning model is established by using stochastic forest classification algorithm, the prediction accuracy is slightly higher than other algorithms. And the random forest algorithm has the characteristics of high robustness, China good performance, strong practicability can effectively carry out water bloom early warning.
EKF based Sliding Mode Control for a Quadrotor Attitude StabilizationOpen Access
Abstract: In recent years, the interest in unmanned aerial vehicles (UAVs) has been increasing around the world. These vehicles are used in various applications from military operations to civilian tasks. Quadrotor, also called as a quadcopter, is one of the different types of UAVs. Quadrotor can fly more stable than helicopter and the flight control is more convenient. In UAVs, the most basic and salient point is the attitude control for stability. This paper estimates quadrotor’s attitude by extended Kalman filter (EKF) and presents the design procedure of a sliding mode control (SMC) to focus on stabilization. The performance and effectiveness of the proposed system are verified through a simulation study.
Study of detection algorithm of pedestrians by image analysis with a crossing request when gazing at a pedestrian crossing signalOpen Access
Abstract: Despite the advancement of information and transportation systems, inconvenient pedestrian crossing buttons remain common. In accordance with intelligent transportation systems (ITS), it is necessary to improve pedestrian crossing systems. Therefore, in this study, the proposed system adopts signal gaze, which is more natural compared to pressing a pedestrian crossing button, as a crossing request. A compact camera is inserted in a traffic light to view the other side of the crosswalk. The image data is analyzed in real time to identify all people who have a crossing request. An algorithm with three detectors using Haar-like feature quantities was developed and an evaluation experiment was conducted, considering three conditions: daytime, nighttime, and shade. The detection rate of crossing requests was 100% within 5 s. Although the detection rate was extremely high, there was a problem of incorrectly detecting non-humans. Therefore, in this research, we evaluated the system when detecting non-humans in order to determine the causes. As a result, it became clear that the detection rate changes rapidly depending on the waiting time for a traffic light and also when crossing the crosswalk; however, the system continues to detect the incorrectly detected background.
Emergence of Robust Cooperative States by Iterative Internalizations of Opponents’ Personalized Values in Minority GameOpen Access
Abstract: Adolescence is a period in which individuals begin facing some challenging choices. Through these choices, and social interactions with peers, adolescent individuals develop their “personalized values” that are the foundations of their actions. It is known that the adolescent brains have high plasticity, and that the adolescent brains change dynamically, other than that of an adult brain. This study discusses the type of behavior that emerges from adolescents, as
well as adult individuals with elevated plasticities. To realize this, we adopt the minority game (MG), which is one of the tasks concerning the choice described above. We implement Elman-nets with different learning rates that express different degrees of the plasticity as the player models in the MG. Our simulation results showed that it is a possibility that robust cooperative states emerge by iteratively internalizing the opponent players’ personalized values among players that have a network of reference relationship, irrespective of their varying degrees of plasticity.
A Sanshin Musical Performance Assistive Device for People with Physical Disabilities of the ExtremitiesOpen Access
Abstract: This paper describes a sanshin musical performance assistive device for people with physical disabilities of the extremities. Sanshin is an Okinawan three-stringed musical instrument. We had developed portable assistive devices to press strings against board of sanshin and its controllers. In the experiments, some participants played the sanshin using the assistive devices. It was found from the experimental results that two persons with muscular dystrophies could appropriately performed music using the sanshins with the previous assistive devices, and that two able-bodied persons could smoothly and steadily play the sanshins using the improved assistive device with the photoelectric sensor-based controller