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- University of Dayton Electro-optics and Photonics (15)
- Atmospheric turbulence (5)
- Image restoration (5)
- Infrared imaging (4)
- Computer Aided Detection (3)
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- Convolutional Neural Networks (3)
- Convolutional neural network (3)
- Optical turbulence (3)
- Turbulence mitigation (3)
- Aliasing (2)
- Anisoplanatic (2)
- Computed Tomography (2)
- Computer-aided detection (2)
- Fischer Linear Discriminant Classifier (2)
- Fusion of interpolated frames (2)
- High resolution (2)
- Imaging (2)
- Long range imaging (2)
- Lung Segmentation (2)
- Optical wave propagation (2)
- Simulation (2)
- Super-resolution (2)
- Support Vector Machine (2)
- Adaptive Wiener filter (1)
- Adaptive signal processing (1)
- Additives (1)
- Aliased images (1)
- Anisoplanatic imaging (1)
- Anisoplanatism (1)
- Anomaly detection (1)
Articles 1 - 30 of 209
Full-Text Articles in Engineering
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …
A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie
A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …
Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu
Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …
Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay
Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay
Electrical and Computer Engineering Faculty Publications
Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …
Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha
Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha
Electrical and Computer Engineering Faculty Publications
Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …
Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu
Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …
Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya
Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya
Electrical and Computer Engineering Faculty Publications
In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …
Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli
Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli
Electrical and Computer Engineering Faculty Publications
Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …
Application Of Tilt Correlation Statistics To Anisoplanatic Optical Turbulence Modeling And Mitigation, Russell C. Hardie, Michael A. Rucci, Santasri Bose-Pillai, Richard Van Hook
Application Of Tilt Correlation Statistics To Anisoplanatic Optical Turbulence Modeling And Mitigation, Russell C. Hardie, Michael A. Rucci, Santasri Bose-Pillai, Richard Van Hook
Electrical and Computer Engineering Faculty Publications
Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation …
Scene Motion Detection In Imagery With Anisoplanatic Optical Turbulence Using A Tilt-Variance-Based Gaussian Mixture Model, Richard L. Van Hook, Russell C. Hardie
Scene Motion Detection In Imagery With Anisoplanatic Optical Turbulence Using A Tilt-Variance-Based Gaussian Mixture Model, Richard L. Van Hook, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
In long-range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image-processing and analysis tasks. The authors present a scenemotion detection algorithm specifically designed to operate in the presence of anisoplanatic optical turbulence. The method models intensity fluctuations in each pixel with a Gaussian mixture model (GMM). The GMM uses knowledge of the turbulence tilt-variance statistics. We provide both quantitative and qualitative performance analyses and compare the proposed method to several state-of-the art algorithms. The image data …
Simulation Of Anisoplanatic Lucky Look Imaging And Statistics Through Optical Turbulence Using Numerical Wave Propagation, Michael A. Rucci, Russell C. Hardie, Richard K. Martin
Simulation Of Anisoplanatic Lucky Look Imaging And Statistics Through Optical Turbulence Using Numerical Wave Propagation, Michael A. Rucci, Russell C. Hardie, Richard K. Martin
Electrical and Computer Engineering Faculty Publications
This paper investigates anisoplanatic numerical wave simulation in the context of lucky look imaging. We demonstrate that numerical wave propagation can produce root mean square (RMS) wavefront distributions and probability of lucky look (PLL) statistics that are consistent with Kolmogorov theory. However, the simulated RMS statistics are sensitive to the sampling parameters used in the propagation window. To address this, we propose and validate a new sample spacing rule based on the point source bandwidth used in the propagation and the level of atmospheric turbulence. We use the tuned simulator to parameterize the wavefront RMS probability density function as a …
Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui
Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui
Electrical and Computer Engineering Faculty Publications
Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …
Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa
Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa
Electrical and Computer Engineering Faculty Publications
We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …
Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch
Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch
Electrical and Computer Engineering Faculty Publications
We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …
Fifnet: A Convolutional Neural Network For Motion-Based Multiframe Super-Resolution Using Fusion Of Interpolated Frames, Hamed Elwarfalli, Russell C. Hardie
Fifnet: A Convolutional Neural Network For Motion-Based Multiframe Super-Resolution Using Fusion Of Interpolated Frames, Hamed Elwarfalli, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
We present a novel motion-based multiframe image super-resolution (SR) algorithm using a convolutional neural network (CNN) that fuses multiple interpolated input frames to produce an SR output. We refer to the proposed CNN and associated preprocessing as the Fusion of Interpolated Frames Network (FIFNET). We believe this is the first such CNN approach in the literature to perform motion-based multiframe SR by fusing multiple input frames in a single network. We study the FIFNET using translational interframe motion with both fixed and random frame shifts. The input to the network is a sequence of interpolated and aligned frames. One key …
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Electrical and Computer Engineering Faculty Publications
Purpose: Diabetic retinopathy is the leading cause of blindness, affecting over 93 million people. An automated clinical retinal screening process would be highly beneficial and provide a valuable second opinion for doctors worldwide. A computer-aided system to detect and grade the retinal images would enhance the workflow of endocrinologists. Approach: For this research, we make use of a publicly available dataset comprised of 3662 images. We present a hybrid machine learning architecture to detect and grade the level of diabetic retinopathy (DR) severity. We also present and compare simple transfer learning-based approaches using established networks such as AlexNet, VGG16, ResNet, …
Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari
Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …
Patch-Based Gaussian Mixture Model For Scene Motion Detection In The Presence Of Atmospheric Optical Turbulence, Richard L. Van Hook, Russell C. Hardie
Patch-Based Gaussian Mixture Model For Scene Motion Detection In The Presence Of Atmospheric Optical Turbulence, Richard L. Van Hook, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
In long-range imaging regimes, atmospheric turbulence degrades image quality. In addition to blurring, the turbulence causes geometric distortion effects that introduce apparent motion in acquired video. This is problematic for image processing tasks, including image enhancement and restoration (e.g., superresolution) and aided target recognition (e.g., vehicle trackers). To mitigate these warping effects from turbulence, it is necessary to distinguish between actual in-scene motion and apparent motion caused by atmospheric turbulence. Previously, the current authors generated a synthetic video by injecting moving objects into a static scene and then applying a well-validated anisoplanatic atmospheric optical turbulence simulator. With known per-pixel truth …
Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such …
Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb
Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb
Electrical and Computer Engineering Faculty Publications
Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …
Fusion Of Interpolated Frames Superresolution In The Presence Of Atmospheric Optical Turbulence, Russell C. Hardie, Michael A. Rucci, Barry K. Karch, Alexander J. Dapore, Douglas R. Droege, Joseph C. French
Fusion Of Interpolated Frames Superresolution In The Presence Of Atmospheric Optical Turbulence, Russell C. Hardie, Michael A. Rucci, Barry K. Karch, Alexander J. Dapore, Douglas R. Droege, Joseph C. French
Electrical and Computer Engineering Faculty Publications
An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this …
A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie
A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs …
A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …
Performance Analysis Of Machine Learning And Deep Learning Architectures For Malaria Detection On Cell Images, Barath Narayanan, Redha Ali, Russell C. Hardie
Performance Analysis Of Machine Learning And Deep Learning Architectures For Malaria Detection On Cell Images, Barath Narayanan, Redha Ali, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images …
Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari
Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …
Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai
Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai
Electrical and Computer Engineering Faculty Publications
To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …
Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari
Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …
Performance Analysis Of Feature Selection Techniques For Support Vector Machine And Its Application For Lung Nodule Detection, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Performance Analysis Of Feature Selection Techniques For Support Vector Machine And Its Application For Lung Nodule Detection, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Electrical and Computer Engineering Faculty Publications
Lung cancer typically exhibits its presence with the formation of pulmonary nodules. Computer Aided Detection (CAD) of such nodules in CT scans would be of valuable help in lung cancer screening. Typical CAD system is comprised of a candidate detector and a feature-based classifier. In this research, we study and explore the performance of Support Vector Machine (SVM) based on a large set of features. We study the performance of SVM as a function of the number of features. Our results indicate that SVM is more robust and computationally faster with a large set of features and less prone to …
Performance Analysis Of A Computer-Aided Detection System For Lung Nodules In Ct At Different Slice Thicknesses, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Performance Analysis Of A Computer-Aided Detection System For Lung Nodules In Ct At Different Slice Thicknesses, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Electrical and Computer Engineering Faculty Publications
We study the performance of a computer-aided detection (CAD) system for lung nodules in computed tomography (CT) as a function of slice thickness. In addition, we propose and compare three different training methodologies for utilizing nonhomogeneous thickness training data (i.e., composed of cases with different slice thicknesses). These methods are (1) aggregate training using the entire suite of data at their native thickness, (2) homogeneous subset training that uses only the subset of training data that matches each testing case, and (3) resampling all training and testing cases to a common thickness. We believe this study has important implications for …