Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis

A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image

The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms

A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space.Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method.Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy.Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals

Real-Time Traffic Light Recognition Based on C-HOG Features

This paper proposes a real-time traffic light detection and recognition algorithm that would allow for the recognition of traffic signals in intelligent vehicles. This algorithm is based on C-HOG features (Color and HOG features) and Support Vector Machine (SVM). The algorithm extracted red and green areas in the video accurately, and then screened the eligible area. Thereafter, the C-HOG features of all kinds of lights could be extracted. Finally, this work used SVM to build a classifier of corresponding category lights. This algorithm obtained accurate real-time information based on the judgment of the decision function. Furthermore, experimental results show that this algorithm demonstrated accuracy and good real-time performance

Evaluating the value of synchrotron radiation phase-contrast CT imaging in the study of knee joint microstructure

The knee joint is one of the tissues that often appear diseased, and the pathogenesis owing to its structure. Therefore, the micro-structure of the knee joint was studied by synchrotron radiation phasecontrast CT imaging (SRPCI-CT). The contrast and the sharpness of the reconstructed slice were calculated and analysed. From these results, the layer structure of the joint tissue can be seen. The cartilage layer (including the meniscus) is about 1.44 mm, and the epiphyseal layer is about 2.25 mm, which conforms to the actual situation of rat knees. From the 3D images, the structure of the meniscus and the epiphyseal bone can be observed. The epiphyseal bone line is also particularly obvious. Simultaneously, the epiphyseal bone also has a mesh structure, which has the characteristics of both cartilage and tibia. Further research on the tibia can determine that the trabecular bone line is about 130 microns. Finally, the bone volume fraction (BVF) of the joint, the data also reflect the existence of a layer structure, and its scale distribution is consistent with the results of sharpness and contrast analysis. The SRPCI-CT can play an important role in the study micro-structure of joints

Particle-Filter-Based Intelligent Video Surveillance System

In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience

Stock discrimination of two European squids (Illex coindetii, Loligo forbesii) by statolith shape analysis

We evaluated the feasibility of classifying ommastrephid and loliginid squid species by statolith shape analysis into fisheries management units or stocks. Statoliths of Illex coindetii (Verany, 1839) and Loligo forbesii (Steenstrup, 1856) were studied from multiple areas of the North East Atlantic and Northern Mediterranean Sea during 2021–2022. I. coindetii and L. forbesii individuals were categorized into multiple stocks across the areas studied. Stocks migrating between fishing areas as well as multiple stocks in the same area, as identified for L. forbesii in this study, need to be considered for sustainable fisheries management

Application-Driven AI Paradigm for Human Action Recognition

Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition

APPLYING DEEP LEARNING METHODS TO IDENTIFY TARGETS IN SYNTHETIC APERTURE RADAR IMAGES

Synthetic aperture radar (SAR) provides high-resolution imagery and can operate in the day and at night and in every weather condition. SAR has been used for military reconnaissance and surveillance. Examining SAR images manually, however, is challenging even for a specialist, since it is difficult to find high-value targets in a wide area of SAR images. This is especially true when time is critical for operations. Thus, an efficient, reliable method to analyze SAR images automatically is needed. To solve this problem, deep learning (DL) methods are developed for automatic target recognition (ATR). A convolutional neural network (CNN) is a deep-learning algorithm made up of several processing layers for target recognition and classification. One of the challenges in developing and testing a CNN algorithm is to find relevant datasets. The dataset used in this thesis comes from the Moving and Stationary Target Acquisition and Recognition program (MSTAR). In this research, the SAR ATR concept and performance are analyzed using several CNN DL architectures. Specifically, this investigation examines the effects of a few variable parameters within CNN DL architectures to gain insight into optimal strategies for using these architectures. Using CNN structures with different numbers of layers, it was possible to classify SAR targets successfully and automatically with state-of-the-art accuracy. This method proved useful for classification and recognition of military targets.Captain, Turkish Air ForceApproved for public release. distribution is unlimite