1
|
Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-Chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2023; 17:1501-1523. [PMID: 37974583 PMCID: PMC10640504 DOI: 10.1007/s11571-022-09897-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/23/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
Collapse
Affiliation(s)
- Afshin Shoeibi
- FPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Juan M. Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
| | | | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| |
Collapse
|
2
|
Ghassemi N, Shoeibi A, Khodatars M, Heras J, Rahimi A, Zare A, Zhang YD, Pachori RB, Gorriz JM. Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Appl Soft Comput 2023; 144:110511. [PMID: 37346824 PMCID: PMC10263244 DOI: 10.1016/j.asoc.2023.110511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/23/2022] [Accepted: 06/08/2023] [Indexed: 06/23/2023]
Abstract
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.
Collapse
Affiliation(s)
- Navid Ghassemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Jonathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain
| | - Alireza Rahimi
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
| | - J Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
- Department of Psychiatry, University of Cambridge, UK
| |
Collapse
|
3
|
Mohamedahmed AYY, Zaman S, Ghassemi N, Ghassemi A, Wuheb AA, Abdalla HEE, Hajibandeh S, Hajibandeh S. Should routine surgical wound drainage after ventral hernia repair be avoided? A systematic review and meta-analysis. Hernia 2023:10.1007/s10029-023-02804-0. [PMID: 37179521 DOI: 10.1007/s10029-023-02804-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023]
Abstract
AIMS To evaluate outcomes of drain use vs. no-drain use during ventral hernia repair. METHODS A PRISMA-compliant systematic review was conducted using the following databases: PubMed, Scopus, Cochrane database, The Virtual Health Library, Clinical trials.gov and Science Direct. Studies comparing use of drains with no-drain during ventral hernia repair (primary or incisional) were included. Wound-related complications, operative time, need for mesh removal and early recurrence were the evaluated outcome parameters. RESULTS Eight studies reporting a total number of two thousand four hundred and sixty-eight patients (drain group = 1214; no-drain group = 1254) were included. The drain group had a significantly higher rate of surgical site infections (SSI) and longer operative time compared with the no-drain group [odds ratio (OR): 1.63, P = 0.01] and [mean difference (MD): 57.30, P = 0.007], respectively. Overall wound-related complications [OR: 0.95, P = 0.88], seroma formation [OR: 0.66, P = 0.24], haematoma occurrence [OR: 0.78, P = 0.61], mesh removal [OR: 1.32, P = 0.74] and early hernia recurrence [OR: 1.10, P = 0.94] did not differ significantly between the two groups. CONCLUSION The available evidence does not seem to support the routine use of surgical drains during primary or incisional ventral hernia repairs. They are associated with increased rates of SSIs and longer total operative time with no significant advantage in terms of wound-related complications.
Collapse
Affiliation(s)
- A Y Y Mohamedahmed
- Department of General Surgery, Royal Wolverhampton NHS Trust, Wolverhampton, West Midlands, UK.
| | - S Zaman
- Department of General Surgery, Sandwell and West, Birmingham Hospitals NHS Trust, Birmingham, West Midlands, UK
| | - N Ghassemi
- Department of General Surgery, Royal Stoke University Hospital, Stoke-On-Trent, UK
| | - A Ghassemi
- School of Medicine and Surgery, Gemelli University Hospital, Catholic University of the Sacred Heart, Rome, Italy
| | - A A Wuheb
- Department of General Surgery, Royal Wolverhampton NHS Trust, Wolverhampton, West Midlands, UK
| | - H E E Abdalla
- Department of General Surgery, Royal Wolverhampton NHS Trust, Wolverhampton, West Midlands, UK
| | - S Hajibandeh
- Department of General Surgery, University Hospital of Wales, Cardiff & Vale NHS Trust, Cardiff, UK
| | - S Hajibandeh
- Department of General Surgery, Royal Stoke University Hospital, Stoke-On-Trent, UK
| |
Collapse
|
4
|
Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Khosravi A, Ling SH, Delfan N, Zhang YD, Wang SH, Gorriz JM, Alinejad-Rokny H, Acharya UR. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review. Comput Biol Med 2023; 160:106998. [PMID: 37182422 DOI: 10.1016/j.compbiomed.2023.106998] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023]
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.
Collapse
Affiliation(s)
- Mahboobeh Jafari
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Afshin Shoeibi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Navid Ghassemi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Parisa Moridian
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia
| | - Niloufar Delfan
- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia; Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| |
Collapse
|
5
|
Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
Collapse
Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| |
Collapse
|
6
|
Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
|
7
|
Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
|
8
|
Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Rajendra Acharya U, Gorriz JM. Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103417] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
9
|
Shoeibi A, Sadeghi D, Moridian P, Ghassemi N, Heras J, Alizadehsani R, Khadem A, Kong Y, Nahavandi S, Zhang YD, Gorriz JM. Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front Neuroinform 2021; 15:777977. [PMID: 34899226 PMCID: PMC8657145 DOI: 10.3389/fninf.2021.777977] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.
Collapse
Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Delaram Sadeghi
- Department of Medical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Islamic Azad University of Science and Research, Tehran, Iran
| | - Navid Ghassemi
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yinan Kong
- School of Engineering, Macquarie University, Sydney, NSW, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester, United Kingdom
| | - Juan Manuel Gorriz
- Department of Signal Theory, Telematics and Communications, ETS of Computer and Telecommunications Engineering, University of Granada, Granada, Spain
| |
Collapse
|
10
|
Javan AAK, Jafari M, Shoeibi A, Zare A, Khodatars M, Ghassemi N, Alizadehsani R, Gorriz JM. Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems. Sensors (Basel) 2021; 21:3925. [PMID: 34200287 PMCID: PMC8200970 DOI: 10.3390/s21113925] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 01/03/2023]
Abstract
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.
Collapse
Affiliation(s)
- Ali Akbar Kekha Javan
- Faculty of Electrical Engineering, Zabol Branch, Islamic Azad University, Zabol 1939598616, Iran;
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran
| | - Marjane Khodatars
- Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad 91735413, Iran;
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia;
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, 52005 Granada, Spain;
| |
Collapse
|
11
|
Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, Khozeimeh F, Zare A, Hosseini-Nejad H, Khosravi A, Atiya AF, Aminshahidi D, Hussain S, Rouhani M, Nahavandi S, Acharya UR. Epileptic Seizures Detection Using Deep Learning Techniques: A Review. Int J Environ Res Public Health 2021; 18:5780. [PMID: 34072232 PMCID: PMC8199071 DOI: 10.3390/ijerph18115780] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 02/06/2023]
Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
Collapse
Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | | | - Navid Ghassemi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, School of Medicine, University of California, San Francisco, CA 94143, USA;
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Hossein Hosseini-Nejad
- Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt;
| | - Diba Aminshahidi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Assam 786004, India;
| | - Modjtaba Rouhani
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Udyavara Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Taichung City 41354, Taiwan
| |
Collapse
|
12
|
Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101678] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
|
13
|
Hemmatiyan S, Rahimi Movassagh M, Ghassemi N, Kargarian M, Rezakhani AT, Langari A. Quantum phase transitions in the Kondo-necklace model: perturbative continuous unitary transformation approach. J Phys Condens Matter 2015; 27:155601. [PMID: 25812634 DOI: 10.1088/0953-8984/27/15/155601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The Kondo-necklace model can describe magnetic low-energy limit of strongly correlated heavy fermion materials. There exist multiple energy scales in this model corresponding to each phase of the system. Here, we study quantum phase transition between the Kondo-singlet phase and the antiferromagnetic long-range ordered phase, and show the effect of anisotropies in terms of quantum information properties and vanishing energy gap. We employ the 'perturbative continuous unitary transformations' approach to calculate the energy gap and spin-spin correlations for the model in the thermodynamic limit of one, two, and three spatial dimensions as well as for spin ladders. In particular, we show that the method, although being perturbative, can predict the expected quantum critical point, where the gap of low-energy spectrum vanishes, which is in good agreement with results of other numerical and Green's function analyses. In addition, we employ concurrence, a bipartite entanglement measure, to study the criticality of the model. Absence of singularities in the derivative of concurrence in two and three dimensions in the Kondo-necklace model shows that this model features multipartite entanglement. We also discuss crossover from the one-dimensional to the two-dimensional model via the ladder structure.
Collapse
Affiliation(s)
- S Hemmatiyan
- Department of Physics, Texas A&M University, College Station, TX 77843-4242, USA. Department of Physics, Sharif University of Technology, Tehran 14588-89694, Iran
| | | | | | | | | | | |
Collapse
|
14
|
|
15
|
Asgary S, Naderi G, Sarrafzadegan N, Ghassemi N, Boshtam M, Rafie M, Arefian A. Anti-oxidant effect of flavonoids on hemoglobin glycosylation. Pharm Acta Helv 1999; 73:223-6. [PMID: 10085787 DOI: 10.1016/s0031-6865(98)00025-9] [Citation(s) in RCA: 63] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
A high glucose concentration has been found to lead to the glycosylation of amino groups of lysine residue in proteins. The addition of reducing agent not only prevents this reaction but also reverses it. On the other hand, flavonoids which found in plant sources have antioxidant properties. Since the glycosylation of protein is an oxidation reaction, therefore, antioxidants should be able to prevent this reaction. In this study, the best concentration and time to incubate glucose with hemoglobin was investigated. Then the glycosylation degree of hemoglobin in the presence of flavonoids and their absence was measured by means of a colorimetric method. Different concentration of flavonoids (Quercetin, Rutin, Kaempferol) were used. The preventing effect on hemoglobin glycosylation by the three concentrations; 0.5, 5, 10 micrograms/ml was estimated as follows: for Rutin; 11%, 27%, 42%, Quercetin; 3%, 37%, 52%, Kaempferol; 10%, 12%, 15% respectively. So, the in vivo effect should be investigated and then plants that containing flavonoids can be utilized to prevent or treat complication of diabetes.
Collapse
Affiliation(s)
- S Asgary
- Isfahan University of Med. Sciences, Health Services Cardiovascular Research Center, Isfahan, Iran.
| | | | | | | | | | | | | |
Collapse
|
16
|
Hölzl J, Ghassemi N, Hahn B. Preparation of 14C-Spiro Ethers by Chamomile and their Use by an Investigation of Absorption. Planta Med 1986:553. [PMID: 17345521 DOI: 10.1055/s-2007-969364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Affiliation(s)
- J Hölzl
- Institut für Pharmazeutische Biologie, 3550 Marburg/L
| | | | | |
Collapse
|