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Item type:Publication, Neutrino interaction classification with a convolutional neural network in the DUNE far detector(American Physical Society, 2020-11-09)The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2--5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of semantic segmentation with few labels in the detection of water bodies from PeruSat-1 satellite’s images(Copernicus Publications, 2020-11-06)Abstract. Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Low-cost image analysis with convolutional neural network for herpes zoster(Elsevier, 2021-10-19)Herpes zoster virus (HZV) or varicella-zoster virus (VZV) affects the trigeminal nerve, at the earliest possible stage will avoid the eyes injuries. In this paper, the new framework develops a new method with convolutional neural networks (CNN), the detection for the early stage of the HZV is tested with 1,000 images. It is 89.6% with low-cost image analysis, besides, the database has been analyzed with other architectures in order to validate the most appropriate algorithm. The process is pre-processing, segmentation, extraction, and classification. The VZV produces two illness: i) Varicella called chickenpox, and ii) Herpes Zoster. In order to obtain a machine learning process, it considers building blocks of convolutional layer neural network associated to a new process for early Herpes Zoster (HZ) disease detection system, structured in four stages as pre-processing, segmentation, extraction and classification. In particular, the new process includes a classification process with a comparison between the K-Nearest Neighborhood (KNN), artificial neural networks (ANN), and logistic model tree (LMT) regression for the comparison. The effectiveness during eight days is 98.1%, for early detection with minimal information. However, the training process produces 33% false positives and an average 90% true positive rate. Early HZ detection and the failures associated with electronic devices were shown and used for facial and pattern recognition associated with nerve location. With this research, the difficulties concerning to the data management and deep learning were corroborated during eight days of the illness, to better understand the process and technology that enable a successful classification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Light-field imaging reconstruction using deep learning enabling intelligent autonomous transportation system(Institute of Electrical and Electronics Engineers Inc., 2021-05-27)Light-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems(MDPI AG, 2021-05-02)Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A feature extraction using probabilistic neural network and BTFSC-Net model with deep learning for brain tumor classification(Multidisciplinary Digital Publishing Institute (MDPI), 2022-12-31)BACKGROUND AND OBJECTIVES: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. MATERIALS AND METHODS: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images' slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. RESULTS: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. CONCLUSIONS: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network(Institute for Ionics, 2022-10-01)Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Affine registration of thermal images of plantar feet using convolutional neural networks(Elsevier Ltd, 2024-09-01)The use of a thermal camera to detect abnormal plantar foot temperature changes can be an effective way to identify the early signs of diabetic foot ulceration. In this work, we performed the affine registration of the plantar foot thermal images using four models based on convolutional neural networks. The process include two parts: an affine registration model for estimating transformation parameters and a spatial transformer for getting the registered image. The performances of the four models were evaluated using the Dice similarity coefficient (DSC), Mean Square Error (MSE), and peak signal-to-noise ratio (PSNR). In the first step, Methods were applied to register the left and right feet of the same subject, called “contralateral registration” and in the second step, the methods were evaluated on a pair of images of the same subject taken in two different times (T0 and T10) using a cold stress test protocol. Results showed that the used convolutional neural networks are robust in both types of registration (contralateral and multitemporal), and they are suitable for the targeted application, with the DSC of 95% for contralateral registration and a DSC of 92% for multitemporal registration. Furthermore, a transversal clinical study was perform on diabetic patients, that classified individuals into ischemic and non-ischemic groups. The objective was to analyze the coherence between the thermal results and medical data. The mean absolute point-to-point temperature difference |ΔT| between left and right foot is lower in non-ischemic patients than in those with ischemia, with p<0.05. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A deep learning approach to distance map generation applied to automatic fiber diameter computation from digital micrographs(Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-01)Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of (Formula presented.) and a mean square error (MSE) of (Formula presented.), demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Semantic Segmentation of Fish and Underwater Environments Using Deep Convolutional Neural Networks and Learned Active Contours(Institute of Electrical and Electronics Engineers Inc., 2023-01-01)The conservation of marine resources requires constant monitoring of the underwater environment by researchers. For this purpose, visual automated monitoring systems are of great interest, especially those that can describe the environment using semantic segmentation based on deep learning. Although they have been successfully used in several applications, such as biomedical ones, obtaining optimal results in underwater environments is still a challenge due to the heterogeneity of water and lighting conditions, and the scarcity of labeled datasets. Even more, the existing deep learning techniques oriented to semantic segmentation only provide low resolution results, lacking the enough spatial details for a high performance monitoring. To address these challenges, a combined loss function based on the active contour theory and level set methods is proposed to refine the spatial segmentation resolution and quality. To evaluate the method, a new underwater dataset with pixel annotations for three classes (fish, seafloor, and water) was created using images from publicly accessible datasets like SUIM, RockFish, and DeepFish. The performance of architectures of convolutional neural networks (CNNs), such as UNet and DeepLabV3+, trained with different loss functions (cross entropy, dice, and active contours) was compared, finding that the proposed combined loss function improved the segmentation results by around 3%, both in the metric Intercept Over Union (IoU) as in Hausdorff Distance (HD).