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Gil J, Choi H, Paeng JC, Cheon GJ, Kang KW. Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data. Nucl Med Mol Imaging 2023; 57:216-222. [PMID: 37720886 PMCID: PMC10504178 DOI: 10.1007/s13139-023-00802-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 03/20/2023] [Accepted: 04/03/2023] [Indexed: 09/19/2023] Open
Abstract
Purpose Deep learning (DL) has been widely used in various medical imaging analyses. Because of the difficulty in processing volume data, it is difficult to train a DL model as an end-to-end approach using PET volume as an input for various purposes including diagnostic classification. We suggest an approach employing two maximum intensity projection (MIP) images generated by whole-body FDG PET volume to employ pre-trained models based on 2-D images. Methods As a retrospective, proof-of-concept study, 562 [18F]FDG PET/CT images and clinicopathological factors of lung cancer patients were collected. MIP images of anterior and lateral views were used as inputs, and image features were extracted by a pre-trained convolutional neural network (CNN) model, ResNet-50. The relationship between the images was depicted on a parametric 2-D axes map using t-distributed stochastic neighborhood embedding (t-SNE), with clinicopathological factors. Results A DL-based feature map extracted by two MIP images was embedded by t-SNE. According to the visualization of the t-SNE map, PET images were clustered by clinicopathological features. The representative difference between the clusters of PET patterns according to the posture of a patient was visually identified. This map showed a pattern of clustering according to various clinicopathological factors including sex as well as tumor staging. Conclusion A 2-D image-based pre-trained model could extract image patterns of whole-body FDG PET volume by using anterior and lateral views of MIP images bypassing the direct use of 3-D PET volume that requires large datasets and resources. We suggest that this approach could be implemented as a backbone model for various applications for whole-body PET image analyses.
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Affiliation(s)
- Joonhyung Gil
- Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
| | - Jin Chul Paeng
- Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
| | - Gi Jeong Cheon
- Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
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Amarnath A, Manoharan P, Natarajan B, Alroobaea R, Alsafyani M, Baqasah AM, Keshta I, Raahemifar K. Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization. Diagnostics (Basel) 2023; 13:2919. [PMID: 37761285 PMCID: PMC10529025 DOI: 10.3390/diagnostics13182919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/20/2023] [Accepted: 05/26/2023] [Indexed: 09/29/2023] Open
Abstract
Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.
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Affiliation(s)
- Ahila Amarnath
- Indian Institute of Technology, Madras, Chennai 600036, Tamilnadu, India
| | - Poongodi Manoharan
- College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar
| | | | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Abdullah M. Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L3G1, Canada
- Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3E9, Canada
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Xu M, Ouyang Y, Yuan Z. Deep Learning Aided Neuroimaging and Brain Regulation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23114993. [PMID: 37299724 DOI: 10.3390/s23114993] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
- Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau SAR 999078, China
| | - Yuanyuan Ouyang
- Nanomicro Sino-Europe Technology Company Limited, Zhuhai 519031, China
- Jiangfeng China-Portugal Technology Co., Ltd., Macau SAR 999078, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau SAR 999078, China
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Agarwal D, Berbís MÁ, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47:57. [PMID: 37129723 PMCID: PMC10154284 DOI: 10.1007/s10916-023-01941-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/20/2023] [Indexed: 05/03/2023]
Abstract
Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.
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Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain.
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Médica, Carmelo Torres No. 2, 23007, Jaén, Spain
| | - Vivian Lipari
- European Atlantic University, Isabel Torres 21, 39011, Santander, Spain
| | | | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain
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Duan J, Liu Y, Wu H, Wang J, Chen L, Chen CLP. Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain. Front Neurosci 2023; 17:1137567. [PMID: 36992851 PMCID: PMC10040750 DOI: 10.3389/fnins.2023.1137567] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/13/2023] [Indexed: 03/14/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted via 2D CNN. BLADNet can search for information over a broad space through the addition of new BLS blocks without retraining of the whole network, thus improving the accuracy of AD classification. Experiments conducted on a dataset containing 2,298 FDG-PET images of 1,045 subjects from the ADNI database demonstrate that our methods are superior to those used in previous studies on early diagnosis of AD with FDG-PET. In particular, our methods achieved state-of-the-art results in EMCI and LMCI classification with FDG-PET.
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Affiliation(s)
- Junwei Duan
- College of Information Science and Technology, Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China
- *Correspondence: Junwei Duan
| | - Yang Liu
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huanhua Wu
- Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jing Wang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
- Jing Wang
| | - Long Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - C. L. Philip Chen
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
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Dalal S, Manoharan P, Lilhore UK, Seth B, Mohammed alsekait D, Simaiya S, Hamdi M, Raahemifar K. Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment. JOURNAL OF CLOUD COMPUTING 2023. [DOI: 10.1186/s13677-022-00356-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
AbstractThere is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication.
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End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer’s Diagnosis. MATHEMATICS 2022. [DOI: 10.3390/math10152575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings.
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Tamil Selvi P., Balasubramaniam K, Vidhya S., Jayapandian N., Ramya K., Poongodi M., Hamdi M, Tunze GB. Social Network User Profiling With Multilayer Semantic Modeling Using Ego Network. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2022. [DOI: 10.4018/ijitwe.304049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Social and information networks undermine the real relationship between the individuals (ego) and the friends (alters) they are connected with on social media. The structure of individual network is highlighted by the ego network. Egocentric approach is popular due to its focus on individuals, groups, or communities. Size, structure, and composition directly impact the ego networks. Moreover, analysis includes strength of ego – alter ties degree and strength of ties. Degree gives the first overview of network. Social support in the network is explored with the “gap” between the degree and average strength. These outcomes firmly propose that, regardless of whether the approaches to convey and to keep up social connections are evolving because of the dispersion of online social networks, the way individuals sort out their social connections appears to remain unaltered. As online social networks evolve, they help in receiving more diverse information.
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Affiliation(s)
- Tamil Selvi P.
- Michael Job College of Arts and Science for Women, India
| | | | | | | | - Ramya K.
- PA College of Engineering and Technology, India
| | - Poongodi M.
- Division of Informational and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mounir Hamdi
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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