136,229 research outputs found

    Neural Network Based Edge Detection for Automated Medical Diagnosis

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    Edge detection is an important but rather difficult task in image processing and analysis. In this research, artificial neural networks are employed for edge detection based on its adaptive learning and nonlinear mapping properties. Fuzzy sets are introduced during the training phase to improve the generalization ability of neural networks. The application of the proposed neural network approach to the edge detection of medical images for automated bladder cancer diagnosis is also investigated. Successful computer simulation results are obtained

    Medical imaging analysis with artificial neural networks

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    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

    Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks

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    Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.Comment: Accepted by AAAI 202

    Lightweight Real-time Detection of Components via a Micro Aerial Vehicle with Domain Randomization Towards Structural Health Monitoring

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    Civil structural component detection plays an integral role in Structural Health Monitoring (SHM) pre and post-construction. Challenges including but not limited to labor-intensiveness, cost, and time constraints associated with traditional methods make it a less opti-mal approach in SHM. Despite the success of deep convolutional neural networks in diverse detection problems, the required computational resources are a challenge. This has led to rendering a chunk of resource-constrained edge nodes less applicable with deep convolutional neural networks. In this paper, a computational-efficient deep convolutional neural network is presented based on Gabor filters and a color Canny edge detector. Generic Gabor filters are generated and used as initializers in the computational-efficient deep convolutional neural network presented, afterward trained on building components data. Next, extensive offline and online experimentation with a resource-constrained edge node is conducted and evaluated using diverse metrics. The computational-efficient detection model demonstrates to be effective in detection and via NVIDIA GPU profiler, we observe conservation of around 30% of computational resources during training. The computational-efficient detection model adduces almost a 3% mean average precision higher than two state-of-the-art detectors and records a promising frame processing rate during the online experimentation

    SICNN optimisation, two dimensional implementation and comparison

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    The study investigates the process of optimisation, implementation and comparison of a Shunting Inhibitory Cellular Neural Network (SICNN) for Edge Detection. Shunting inhibition is lateral inhibition where the inhibition function is nonlinear. Cellular Neural Networks are locally interconnected nonlinear, parallel networks which can exist as either discrete time or continuous networks. The name given to Cellular Neural Networks that use shunting inhibition as their nonlinear cell interactions are called Shunting Inhibitory Cellular Neural Networks. This project report examines some existing edge detectors and thresholding techniques. Then it describes the optimisation of the connection weight matrix for SICNN with Complementary Output Processing and SICNN with Division Output Processing. The parameter values of this optimisation as well as the thresholding methods studied are used in software implementation of the SICNN. This-two dimensional SICNN edge detector is then compared to some other common edge detectors, namely the Sobel and Canny detectors. It was found that the SICNN with complementary output processing performed as well or better than the two other detectors. The SICNN was also very flexible in being able to be easily modified to deal with different image conditions
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