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Article ; Online: QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

Ahmadi, Mohsen / Sharifi, Abbas / Hassantabar, Shayan / Enayati, Saman

2021 Volume 2021, Page(s) 6653879

Abstract: Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, ...

Abstract	Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods' reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.
MeSH term(s)	Algorithms ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/pathology ; Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/instrumentation ; Magnetic Resonance Imaging/methods ; Magnetic Resonance Imaging/standards ; Neural Networks, Computer ; ROC Curve ; Signal-To-Noise Ratio
Language	English
Publishing date	2021-01-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2698540-8
ISSN	2314-6141 ; 2314-6133
ISSN (online)	2314-6141
ISSN	2314-6133
DOI	10.1155/2021/6653879
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: QAIS-DSNN

Mohsen Ahmadi / Abbas Sharifi / Shayan Hassantabar / Saman Enayati

BioMed Research International, Vol

Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

2021 Volume 2021

Abstract	Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods’ reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.
Keywords	Medicine ; R
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches.

Hassantabar, Shayan / Ahmadi, Mohsen / Sharifi, Abbas

Chaos, solitons, and fractals

2020 Volume 140, Page(s) 110170

Abstract: COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected ...

Abstract	COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.
Keywords	covid19
Language	English
Publishing date	2020-07-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110170
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches

Hassantabar, Shayan / Ahmadi, Mohsen / Sharifi, Abbas

Chaos, Solitons & Fractals

2020 Volume 140, Page(s) 110170

Keywords	General Mathematics ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110170
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches

Hassantabar, Shayan / Ahmadi, Mohsen / Sharifi, Abbas

Chaos Solitons Fractals

Abstract	COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #684371
Database	COVID19

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Book ; Online: TUTOR

Hassantabar, Shayan / Terway, Prerit / Jha, Niraj K.

Training Neural Networks Using Decision Rules as Model Priors

2020

Abstract: The human brain has the ability to carry out new tasks with limited experience. It utilizes prior learning experiences to adapt the solution strategy to new domains. On the other hand, deep neural networks (DNNs) generally need large amounts of data and ... ...

Abstract	The human brain has the ability to carry out new tasks with limited experience. It utilizes prior learning experiences to adapt the solution strategy to new domains. On the other hand, deep neural networks (DNNs) generally need large amounts of data and computational resources for training. However, this requirement is not met in many settings. To address these challenges, we propose the TUTOR DNN synthesis framework. TUTOR targets non-image datasets. It synthesizes accurate DNN models with limited available data, and reduced memory and computational requirements. It consists of three sequential steps: (1) drawing synthetic data from the same probability distribution as the training data and labeling the synthetic data based on a set of rules extracted from the real dataset, (2) use of two training schemes that combine synthetic data and training data to learn DNN weights, and (3) employing a grow-and-prune synthesis paradigm to learn both the weights and the architecture of the DNN to reduce model size while ensuring its accuracy. We show that in comparison with fully-connected DNNs, on an average TUTOR reduces the need for data by 6.0x (geometric mean), improves accuracy by 3.6%, and reduces the number of parameters (floating-point operations) by 4.7x (4.3x) (geometric mean). Thus, TUTOR is a less data-hungry, accurate, and efficient DNN synthesis framework. Comment: 12 pages, 4 figures
Keywords	Computer Science - Neural and Evolutionary Computing ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-10-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SCANN

Hassantabar, Shayan / Wang, Zeyu / Jha, Niraj K.

Synthesis of Compact and Accurate Neural Networks

2019

Abstract: Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually ...

Abstract	Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually by exploring its hyperparameter space and kept fixed during training. This approach is time-consuming and inefficient. Another issue is that modern neural networks often contain millions of parameters, whereas many applications and devices require small inference models. However, efforts to migrate DNNs to such devices typically entail a significant loss of classification accuracy. To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs. At the core of our framework is the SCANN methodology that uses three basic architecture-changing operations, namely connection growth, neuron growth, and connection pruning, to synthesize feed-forward architectures with arbitrary structure. SCANN encapsulates three synthesis methodologies that apply a repeated grow-and-prune paradigm to three architectural starting points. DR+SCANN combines the SCANN methodology with dataset dimensionality reduction to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN and DR+SCANN on various image and non-image datasets. We evaluate SCANN on MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to medium-size datasets. We also show that our synthesis methodology yields neural networks that are much better at navigating the accuracy vs. energy efficiency space. This would enable neural network-based inference even on Internet-of-Things sensors. Comment: 13 pages, 8 figures
Keywords	Computer Science - Neural and Evolutionary Computing ; Computer Science - Machine Learning
Subject code	006
Publishing date	2019-04-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: STEERAGE

Hassantabar, Shayan / Dai, Xiaoliang / Jha, Niraj K.

Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods

2019

Abstract: Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks are known to have a lot of redundancy. This increases the computational ... ...

Abstract	Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks are known to have a lot of redundancy. This increases the computational cost of inference and poses an obstacle to deployment on Internet-of-Thing sensors and edge devices. To address these challenges, we propose the STEERAGE synthesis methodology. It consists of two complementary approaches: efficient architecture search, and grow-and-prune NN synthesis. The first step, covered in a global search module, uses an accuracy predictor to efficiently navigate the architectural search space. The predictor is built using boosted decision tree regression, iterative sampling, and efficient evolutionary search. The second step involves local search. By using various grow-and-prune methodologies for synthesizing convolutional and feed-forward NNs, it reduces the network redundancy, while boosting its performance. We have evaluated STEERAGE performance on various datasets, including MNIST and CIFAR-10. On MNIST dataset, our CNN architecture achieves an error rate of 0.66%, with 8.6x fewer parameters compared to the LeNet-5 baseline. For the CIFAR-10 dataset, we used the ResNet architectures as the baseline. Our STEERAGE-synthesized ResNet-18 has a 2.52% accuracy improvement over the original ResNet-18, 1.74% over ResNet-101, and 0.16% over ResNet-1001, while having comparable number of parameters and FLOPs to the original ResNet-18. This shows that instead of just increasing the number of layers to increase accuracy, an alternative is to use a better NN architecture with fewer layers. In addition, STEERAGE achieves an error rate of just 3.86% with a variant of ResNet architecture with 40 layers. To the best of our knowledge, this is the highest accuracy obtained by ResNet-based architectures on the CIFAR-10 dataset. Comment: 12 pages, 4 figures
Keywords	Computer Science - Neural and Evolutionary Computing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2019-12-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

Hassantabar, Shayan / Stefano, Novati / Ghanakota, Vishweshwar / Ferrari, Alessandra / Nicola, Gregory N. / Bruno, Raffaele / Marino, Ignazio R. / Hamidouche, Kenza / Jha, Niraj K.

Abstract: The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive ... ...

Abstract	The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic, and symptomatic patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. We also augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: CovidDeep

Hassantabar, Shayan / Stefano, Novati / Ghanakota, Vishweshwar / Ferrari, Alessandra / Nicola, Gregory N. / Bruno, Raffaele / Marino, Ignazio R. / Hamidouche, Kenza / Jha, Niraj K.

SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

2020

Abstract	The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic, and symptomatic patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. We also augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations. Comment: 11 pages, 3 figures
Keywords	Computer Science - Human-Computer Interaction ; Computer Science - Neural and Evolutionary Computing ; covid19
Publishing date	2020-07-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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