Neural Network Architectures For Detection Research Articles

Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning

ObjectiveThe prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR.Materials and methodsThis study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls.ResultsThe displacement between predicted and annotated landmark had a standard deviation of 8–17 mm and was larger than the interobserver variability by a factor of 1.1–2.0. A high overall classification accuracy of 98.7% was achieved.DiscussionDecoupling the identification of clinically meaningful anatomic landmarks from the actual classification improved transparency of classification results. Information from such automated analysis could be used to quickly jump to anatomic positions and guide the physician more efficiently through the analysis depending on the detected condition, which may ultimately improve work flow and save analysis time.

Attention-Enabled Lightweight Neural Network Architecture for Detection of Action Unit Activation

Attention-Enabled Lightweight Neural Network Architecture for Detection of Action Unit Activation

Convolutional neural network architecture for detection and classification of diseases in fruits

Convolutional neural network architecture for detection and classification of diseases in fruits

DEEP LEARNING BASED BRAIN TUMOR CLASSIFICATION USING MAGNETIC RESONANCE IMAGING

Brain tumor means the aggregation of abnormal cells in some tissues of the brain. Brain tumor can be cancerous or noncancerous. The most common types of brain tumors are Glioma, Meningioma and Pituitary tumor. Early detection of tumor cells plays a major role in treatment and recovery of patient. Diagnosing a brain tumor usually undergoes a very complicated and time consuming process. The MRI images of various patients at various stages can be used for the detection of tumors. There are various types of feature extraction and classification methods which are used for detection of brain tumor from MRI images. Convolutional Neural Network image classification algorithm helps in detecting the tumor at early stage with high accuracy. We proposed a Recurrent Neural Network architecture for detection of tumor cells which gives accuracy of about 90%. A recurrent neural network (RNN) is a type of artificial neural networks in that connections between nodes form a directed graph along a temporal sequence.

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches.

Low Power VLSI based Design and Implementation of MLP-BP Neural Network for Detection of Kidney Stone in Real Time

In recent years one of the most common problems that occur in the human urinary system is kidney stones or urinary stones. There are many Methods of medical imaging that can be used to examine parameters of human kidneys, for example magnetic resonance imaging (MRI), x-ray computed tomography (CT), ultrasound imaging (US), and many others. This detection is very important for the doctor to determine the status of the kidneys and also to visualize any abnormalities present in the kidney [2]. Any person affected with a problem in kidney suffers with a pain in early stage. The detection of abnormalities of kidney inside the body is a main field of study in medical research by bio-medical image processing[1-4], Due to some abnormalities (speckle noise) in ultrasound or MRI images and artifacts, wrong diagnosis may happen by analyzing the scanned image. Therefore in this work the main focus is on development of new hardware implementation based on neural network architecture for detection of kidney stone in real time by optimizing area, power and speed on FPGA [5]. This algorithm implemented on Vertex-II Pro FPGA device and simulated in matlab [9].

Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data

This paper describes the development of neural network models for automatic incident detection on arterial roads, using simulated data derived from inductive loop detectors and probe vehicles. The work reported in this paper extends previous research by comparing the performance of various data fusion neural network architectures and assessing model performance for various probe vehicle penetration rates and loop detector configurations. Data from 108 incidents was collected from loop detectors and probe vehicles using a calibrated and validated traffic simulation model. The best performance was obtained for detector configurations found on most existing road networks, with a detection rate of 86%, false alarm rate of 0.36% and probe vehicle penetration rate of 20%. Fusion of speed data further improved performance, resulting in an incident detection rate of 90% and a false alarm rate of 0.5%. The results reported in this paper demonstrate the feasibility of developing advanced data fusion neural network architectures for detection of incidents on urban arterials using data from existing loop detector configurations and probe vehicles.

Modular neural network architecture for detection of operational problems on urban arterials : Khan, Sarosh Islam, Ph.D. University of California, Irvine, 1995. 112 pp. Chair: Stephen G. Ritchie

In recent years, transportation research has revealed that problems of widespread congestion cannot be solved by building more roads or by expanding existing infrastructure. A significant part of the solution lies in better management of traffic. One of the principal thrusts of the new national program on Intelligent Transportation Systems (ITS) is Advanced Transportation Management Systems (ATMS). To facilitate better management, recent research has focused on continuous monitoring of traffic to ascertain the 'normal' level of congestion and to provide an understanding of how it forms and spreads. Techniques for rapidly detecting incidents have become a vital link in the management of traffic.