Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study

AbstractThe Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.

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An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽

10.3390/technologies9010014 ◽

2021 ◽

Vol 9 (1) ◽

pp. 14

Author(s):

James Dzisi Gadze ◽

Akua Acheampomaa Bamfo-Asante ◽

Justice Owusu Agyemang ◽

Henry Nunoo-Mensah ◽

Kwasi Adu-Boahen Opare

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Learning Algorithm ◽

Single Point ◽

Denial Of Service ◽

Specific Work ◽

Learning Models ◽

Ddos Attack ◽

Deep Learning Algorithm ◽

Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

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optNet-50: An Optimized Residual Neural Network Architecture of Deep Learning for Driver's Distraction

2020 IEEE 23rd International Multitopic Conference (INMIC) ◽

10.1109/inmic50486.2020.9318087 ◽

2020 ◽

Author(s):

Tahir Abbas ◽

Syed Farooq Ali ◽

Aadil Zia Khan ◽

Irfan Kareem

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Architecture ◽

Neural Network Architecture

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A deep learning approach for 18F-FDG PET attenuation correction

EJNMMI Physics ◽

10.1186/s40658-018-0225-8 ◽

2018 ◽

Vol 5 (1) ◽

Cited By ~ 26

Author(s):

Fang Liu ◽

Hyungseok Jang ◽

Richard Kijowski ◽

Gengyan Zhao ◽

Tyler Bradshaw ◽

...

Keyword(s):

Deep Learning ◽

Attenuation Correction ◽

Fdg Pet ◽

Learning Approach ◽

18F Fdg

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Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks

EJNMMI Research ◽

10.1186/s13550-020-00644-y ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Karim Armanious ◽

Tobias Hepp ◽

Thomas Küstner ◽

Helmut Dittmann ◽

Konstantin Nikolaou ◽

...

Keyword(s):

Deep Learning ◽

Attenuation Correction ◽

Fdg Pet ◽

Whole Body ◽

Generative Adversarial Networks ◽

Learning Approach ◽

Adversarial Networks ◽

18F Fdg

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Deep Learning Versus Logistic Regression to Predict Opioid Dose Reduction After Spinal Cord Stimulation

Neurosurgery ◽

10.1093/neuros/nyaa447_638 ◽

2020 ◽

Vol 67 (Supplement_1) ◽

Author(s):

Syed M Adil ◽

Lefko T Charalambous ◽

Kelly R Murphy ◽

Shervin Rahimpour ◽

Stephen C Harward ◽

...

Keyword(s):

Neural Network ◽

Spinal Cord ◽

Deep Learning ◽

Dose Reduction ◽

Spinal Cord Stimulation ◽

Network Architecture ◽

Characteristic Curve ◽

Model Performance ◽

Health Crisis ◽

Opioid Dose

Abstract INTRODUCTION Opioid misuse persists as a public health crisis affecting approximately one in four Americans.1 Spinal cord stimulation (SCS) is a neuromodulation strategy to treat chronic pain, with one goal being decreased opioid consumption. Accurate prognostication about SCS success is key in optimizing surgical decision making for both physicians and patients. Deep learning, using neural network models such as the multilayer perceptron (MLP), enables accurate prediction of non-linear patterns and has widespread applications in healthcare. METHODS The IBM MarketScan® (IBM) database was queried for all patients ≥ 18 years old undergoing SCS from January 2010 to December 2015. Patients were categorized into opioid dose groups as follows: No Use, ≤ 20 morphine milligram equivalents (MME), 20–50 MME, 50–90 MME, and >90 MME. We defined “opiate weaning” as moving into a lower opioid dose group (or remaining in the No Use group) during the 12 months following permanent SCS implantation. After pre-processing, there were 62 predictors spanning demographics, comorbidities, and pain medication history. We compared an MLP with four hidden layers to the LR model with L1 regularization. Model performance was assessed using area under the receiver operating characteristic curve (AUC) with 5-fold nested cross-validation. RESULTS Ultimately, 6,124 patients were included, of which 77% had used opioids for >90 days within the 1-year pre-SCS and 72% had used >5 types of medications during the 90 days prior to SCS. The mean age was 56 ± 13 years old. Collectively, 2,037 (33%) patients experienced opiate weaning. The AUC was 0.74 for the MLP and 0.73 for the LR model. CONCLUSION To our knowledge, we present the first use of deep learning to predict opioid weaning after SCS. Model performance was slightly better than regularized LR. Future efforts should focus on optimization of neural network architecture and hyperparameters to further improve model performance. Models should also be calibrated and externally validated on an independent dataset. Ultimately, such tools may assist both physicians and patients in predicting opioid dose reduction after SCS.

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Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

Applied Sciences ◽

10.3390/app11157148 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7148

Author(s):

Bedada Endale ◽

Abera Tullu ◽

Hayoung Shi ◽

Beom-Soo Kang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Input Data ◽

Deep Neural Network ◽

Data Augmentation ◽

Object Classification ◽

Training Data ◽

Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽

10.3390/s21082629 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2629

Author(s):

Kunkyu Lee ◽

Min Kim ◽

Changhyun Lim ◽

Tai-Kyong Song

Keyword(s):

Deep Learning ◽

Ultrasound Imaging ◽

Mobile Device ◽

Network Architecture ◽

Point Of Care ◽

Ultrasound Image ◽

Training Dataset ◽

Point Of Care Ultrasound ◽

Reverse Scan ◽

Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

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