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Chaitanya MK, Sharma LD. Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier. Comput Methods Biomech Biomed Engin 2023:1-17. [PMID: 37861426 DOI: 10.1080/10255842.2023.2270101] [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: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/21/2023]
Abstract
Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy (A c % ) of 99.8, sensitivity (S e % ) of 99, and 100 specificity (Sp%) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean A c % , Se%, and Sp% of 85.2, 83.1, and 84.5, respectively.
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Affiliation(s)
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, India
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2
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Hammad M, Pławiak P, ElAffendi M, El-Latif AAA, Latif AAA. Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:7295. [PMID: 37631831 PMCID: PMC10457904 DOI: 10.3390/s23167295] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/04/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023]
Abstract
This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.
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Affiliation(s)
- Mohamed Hammad
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.E.); (A.A.A.E.-L.)
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 St., 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Mohammed ElAffendi
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.E.); (A.A.A.E.-L.)
| | - Ahmed A. Abd El-Latif
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.E.); (A.A.A.E.-L.)
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom 32511, Egypt
| | - Asmaa A. Abdel Latif
- Industrial Medicine and Occupational Health Division, Public Health and Community Medicine Department, Faculty of Medicine, Menoufia University, Shebin El Kom 32511, Egypt;
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Umar U, Nayab S, Irfan R, Khan MA, Umer A. E-Cardiac Care: A Comprehensive Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8073. [PMID: 36298423 PMCID: PMC9610906 DOI: 10.3390/s22208073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.
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Affiliation(s)
- Umara Umar
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Sanam Nayab
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Rabia Irfan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid i Azam University, Islamabad 45320, Pakistan
| | - Amna Umer
- Department of Computational Sciences, The University of Faisalabad (TUF), Faisalabad 38000, Pakistan
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4
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El-Rahiem BA, El-Samie FEA, Amin M. Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein. MULTIMEDIA SYSTEMS 2022; 28:1325-1337. [DOI: 10.1007/s00530-021-00810-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/13/2021] [Indexed: 09/01/2023]
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5
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Hammad M, Alkinani MH, Gupta BB, Abd El-Latif AA. Myocardial infarction detection based on deep neural network on imbalanced data. MULTIMEDIA SYSTEMS 2022; 28:1373-1385. [DOI: 10.1007/s00530-020-00728-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/01/2020] [Indexed: 09/02/2023]
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Habib M, Ramzan M, Khan SA. A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging. JOURNAL OF GRID COMPUTING 2022; 20:23. [PMID: 35874855 PMCID: PMC9294765 DOI: 10.1007/s10723-022-09615-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.
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Affiliation(s)
- Mohammed Habib
- Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673 Riyadh, Saudi Arabia
- Department of Electrical Engineering, Faculty of Engineering, PortSaid University, Port Said, 42526 Egypt
| | - Muhammad Ramzan
- Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673 Riyadh, Saudi Arabia
| | - Sajid Ali Khan
- Department of Software Engineering, Foundation University Islamabad, 44000 Islamabad, Pakistan
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Musa N, Gital AY, Aljojo N, Chiroma H, Adewole KS, Mojeed HA, Faruk N, Abdulkarim A, Emmanuel I, Folawiyo YY, Ogunmodede JA, Oloyede AA, Olawoyin LA, Sikiru IA, Katb I. A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:9677-9750. [PMID: 35821879 PMCID: PMC9261902 DOI: 10.1007/s12652-022-03868-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/26/2022] [Indexed: 06/08/2023]
Abstract
The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. Supplementary information The online version contains supplementary material available at 10.1007/s12652-022-03868-z.
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Affiliation(s)
- Nehemiah Musa
- Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
| | - Abdulsalam Ya’u Gital
- Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
| | | | - Haruna Chiroma
- Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia
- Computer Science and Engineering , University of Hafr Al-Batin, Hafr Al-Batin, Saudi Arabia
| | - Kayode S. Adewole
- Department of Computer Science, University of Ilorin, Ilorin, Nigeria
| | - Hammed A. Mojeed
- Department of Computer Science, University of Ilorin, Ilorin, Nigeria
| | - Nasir Faruk
- Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria
| | - Abubakar Abdulkarim
- Department of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, Nigeria
| | - Ifada Emmanuel
- Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria
| | | | | | | | | | | | - Ibrahim Katb
- Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia
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Umair MB, Iqbal Z, Faraz MA, Khan MA, Zhang YD, Razmjooy N, Kadry S. A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model. BIG DATA 2022. [PMID: 35704031 DOI: 10.1089/big.2021.0268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.
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Affiliation(s)
- Muhammad Basit Umair
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Pakistan
| | - Zeshan Iqbal
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Pakistan
| | | | | | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester, United Kingdom
| | | | - Sefedine Kadry
- Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
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9
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Fang R, Lu CC, Chuang CT, Chang WH. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106762. [PMID: 35378394 DOI: 10.1016/j.cmpb.2022.106762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/04/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method. METHODS We apply a multi-VGG deep convolutional neural network to top-view images of 3-D ECG, which are generated from simply denoised standard 12 leads ECG signals for classification. The multi-network method, which separately classifies QRS areas, ST areas, and whole heartbeats, was applied to improve classification performance. Furthermore, the Grad-CAM++ method was used to provide visually interpretable heatmaps for user's attention to improve network interpretability and assist physicians in MI diagnosis RESULTS: The proposed method achieved 95.65% inter-patient accuracy and exactly perfect inner-patient accuracy in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database experiment. In the PTB-XL diagnostic ECG database, the proposed method achieved 97.23% inter-patient accuracy. The Grad-CAM++ experiment results also showed that the highlighted areas matched the medical diagnosis criteria for myocardial infarction. CONCLUSIONS Our method demonstrates that 3-D ECG images with AI classification can be efficiently employed for heart disease diagnosis with both high accuracy and visual interpretability.
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Affiliation(s)
- Rui Fang
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Chih-Cheng Lu
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
| | - Cheng-Ta Chuang
- Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Han Chang
- Emergency Department, Mackay Memorial Hospital, Taipei 10449, Taiwan
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10
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Efficient cancellable multi-biometric recognition system based on deep learning and bio-hashing. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03153-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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11
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Masood S, Khan R, Abd El-Latif AA, Ahmad M. An FCN-LSTM model for neurological status detection from non-invasive multivariate sensor data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07117-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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12
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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Sujatha D, Subramaniam M, Rene Robin CR. A new design of multimedia big data retrieval enabled by deep feature learning and Adaptive Semantic Similarity Function. MULTIMEDIA SYSTEMS 2022; 28:1039-1058. [PMID: 35153387 PMCID: PMC8817669 DOI: 10.1007/s00530-022-00897-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases "(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.
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Affiliation(s)
- D. Sujatha
- Information Technology, St. Peter’s College of Engineering and Technology, Chennai, India
| | - M. Subramaniam
- Dept. of Information Technology, SreeVidyanikethan Engineering College, Tirupati, 517 102 India
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Zhang H, Zhang H. LungSeek: 3D Selective Kernel residual network for pulmonary nodule diagnosis. THE VISUAL COMPUTER 2022; 39:679-692. [PMID: 35103029 PMCID: PMC8792456 DOI: 10.1007/s00371-021-02366-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 06/14/2023]
Abstract
Early detection and diagnosis of pulmonary nodules is the most promising way to improve the survival chances of lung cancer patients. This paper proposes an automatic pulmonary cancer diagnosis system, LungSeek. LungSeek is mainly divided into two modules: (1) Nodule detection, which detects all suspicious nodules from computed tomography (CT) scan; (2) Nodule Classification, classifies nodules as benign or malignant. Specifically, a 3D Selective Kernel residual network (SK-ResNet) based on the Selective Kernel Network and 3D residual network is located. A deep 3D region proposal network with SK-ResNet is designed for detection of pulmonary nodules while a multi-scale feature fusion network is designed for the nodule classification. Both networks use the SK-Net module to obtain different receptive field information, thereby effectively learning nodule features and improving diagnostic performance. Our method has been verified on the luna16 data set, reaching 89.06, 94.53% and 97.72% when the average number of false positives is 1, 2 and 4, respectively. Meanwhile, its performance is better than the state-of-the-art method and other similar networks and experienced doctors. This method has the ability to adaptively adjust the receptive field according to multiple scales of the input information, so as to better detect nodules of various sizes. The framework of LungSeek based on 3D SK-ResNet is proposed for nodule detection and nodule classification from chest CT. Our experimental results demonstrate the effectiveness of the proposed method in the diagnosis of pulmonary nodules.
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Affiliation(s)
- Haowan Zhang
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081 China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Hong Zhang
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081 China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
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15
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Abounassar EM, El-Kafrawy P, Abd El-Latif AA. Security and Interoperability Issues with Internet of Things (IoT) in Healthcare Industry: A Survey. STUDIES IN BIG DATA 2022:159-189. [DOI: 10.1007/978-3-030-85428-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Moitra D, Mandal RK. Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:10279-10297. [PMID: 35194379 PMCID: PMC8852869 DOI: 10.1007/s11042-022-12229-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/12/2021] [Accepted: 01/14/2022] [Indexed: 05/04/2023]
Abstract
Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. Most of the studies have considered a particular tumor genre categorized according to its originating organ. This has enriched the domain-specific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. The present research work has prepared a heterogeneous tumor dataset comprising eight different datasets from The Cancer Imaging Archives and classified them according to their respective stages, as suggested by the American Joint Committee on Cancer. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis stages. A new non-sequential deep hybrid model ensemble has been developed by exploiting branched and re-injected layers, followed by bidirectional recurrent layers to classify tumor images. Results have been compared with standard sequential deep learning models and notable recent studies. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The proposed model may help radiologists by acting as an auxiliary decision support system and speed up the tumor diagnosis process.
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18
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El-Rahiem BA, Amin M, Sedik A, Samie FEAE, Iliyasu AM. An efficient multi-biometric cancellable biometric scheme based on deep fusion and deep dream. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 13:2177-2189. [PMID: 34745376 PMCID: PMC8559428 DOI: 10.1007/s12652-021-03513-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Today, biometrics are the preferred technologies for person identification, authentication, and verification cutting across different applications and industries. Sadly, this ubiquity has invigorated criminal efforts aimed at violating the integrity of these modalities. Our study presents a multi-biometric cancellable scheme (MBCS) that exploits the proven utility of deep learning models to fuse multi-exposure fingerprint, finger vein, and iris biometrics by using an Inspection V3 pre-trained model to generate an aggregate tamper-proof cancellable template. To validate our MBCS, we employed an extensive evaluation including visual, quantitative, and qualitative assessments as well as complexity analysis where average outcomes of 99.158%, 24.523 dB, 0.079, 0.909, 59.582 and 23.627 were recorded for NPCR, PSNR, SSIM, UIQ, SD and UACI respectively. These quantitative outcomes indicate that the proposed scheme compares favourably against state-of-the-art methods reported in the literature. To further improve the utility of the proposed MBCS, we are exploring its refinement to facilitate generation of cancellable templates for real-time biometric applications in person authentication at airports, banks, etc.
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Affiliation(s)
- Basma Abd El-Rahiem
- Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt
| | - Mohamed Amin
- Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt
| | - Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, 33511 Egypt
| | - Fathi E. Abd El Samie
- Department of Electronics and Electrical Communications Engineering, Menoufia University, Menouf, Egypt
| | - Abdullah M. Iliyasu
- Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942 Saudi Arabia
- School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502 Japan
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022 China
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19
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A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4243700. [PMID: 34567101 PMCID: PMC8463188 DOI: 10.1155/2021/4243700] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/09/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022]
Abstract
The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.
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20
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Tu S, Zhang M. Research on planning and design of settlement from cities to rural areas based on big data technology. Soft comput 2021. [DOI: 10.1007/s00500-021-06246-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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21
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Iniguez-Lomeli FJ, Bornat Y, Renaud S, Barron-Zambrano JH, Rostro-Gonzalez H. A real-time FPGA-based implementation for detection and sorting of bio-signals. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05853-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Ding H, Chen L, Tao Q, Fu Z, Dong L, Cui X. DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. Neural Comput Appl 2021; 35:5015-5031. [PMID: 34404963 PMCID: PMC8359769 DOI: 10.1007/s00521-021-06329-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/10/2021] [Indexed: 11/27/2022]
Abstract
The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.
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Affiliation(s)
- Hongwei Ding
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
- Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, China
| | - Leiyang Chen
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Qi Tao
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Zhongwang Fu
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Liang Dong
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Xiaohui Cui
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
- Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, China
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23
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Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02696-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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24
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Syrkiewicz-Świtała M, Detyna B, Sosada N, Detyna J, Świtała R, Bitkowska A, Szkutnik J. Mobile applications and eating habits among women and men – Polish experiences. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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25
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An effective mobile-healthcare emerging emergency medical system using conformable chaotic maps. Soft comput 2021. [DOI: 10.1007/s00500-021-05781-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5584004. [PMID: 33997017 PMCID: PMC8112909 DOI: 10.1155/2021/5584004] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 12/17/2022]
Abstract
Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.
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Affiliation(s)
- Venkatesan Chandran
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - M. G. Sumithra
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Alagar Karthick
- Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Tony George
- Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology Mattoor, Kalady, Kerala 683574, India
| | - M. Deivakani
- Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, 624622 Tamilnadu, India
| | - Balan Elakkiya
- Department of Electronics and Communication Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Tamilnadu 600062, India
| | - Umashankar Subramaniam
- Department of Communications and Networks, Renewable Energy Lab, College of Engineering, Prince, Sultan University, Riyadh 12435, Saudi Arabia
| | - S. Manoharan
- Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo, Post Box No. 19, Ethiopia
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27
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Sedik A, Hammad M, Abd El-Samie FE, Gupta BB, Abd El-Latif AA. Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput Appl 2021; 34:11423-11440. [PMID: 33487885 PMCID: PMC7814271 DOI: 10.1007/s00521-020-05410-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/29/2020] [Indexed: 02/04/2023]
Abstract
The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, Egypt
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, 32952 Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428 Saudi Arabia
| | - Brij B. Gupta
- National Institute of Technology, Kurukshetra, India
- Department of Computer Science and Information Engineering, Asia University, Taichung City, Taiwan
| | - Ahmed A. Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebeen El-Kom, 32511 Egypt
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28
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Hammad M, Iliyasu AM, Subasi A, Ho ESL, El-Latif AAA. A Multitier Deep Learning Model for Arrhythmia Detection. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:1-9. [PMID: 0 DOI: 10.1109/tim.2020.3033072] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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29
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Sedik A, Tawalbeh L, Hammad M, El-Latif AAA, El-Banby GM, Khalaf AAM, El-Samie FEA, Iliyasu AM. Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities. IEEE ACCESS 2021; 9:94780-94788. [DOI: 10.1109/access.2021.3088341] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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30
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Sedik A, Iliyasu AM, Abd El-Rahiem B, Abdel Samea ME, Abdel-Raheem A, Hammad M, Peng J, Abd El-Samie FE, Abd El-Latif AA. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses 2020; 12:E769. [PMID: 32708803 PMCID: PMC7411959 DOI: 10.3390/v12070769] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/01/2020] [Accepted: 07/14/2020] [Indexed: 12/16/2022] Open
Abstract
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt;
| | - Abdullah M Iliyasu
- Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
- School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
| | - Basma Abd El-Rahiem
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt;
- Centre for Excellence in Cybersecurity, Quantum Information Processing, and Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt
| | - Mohammed E. Abdel Samea
- Medical Imaging and Interventional Radiology Departement, National Liver Institute, Menoufia university, Shebin El-Koom 32511, Egypt;
| | - Asmaa Abdel-Raheem
- Public Health and Community Medicine Department, Faculty of Medicine Menoufia University, Shebin El-Koom 32511, Egypt;
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebin El-Koom 32511, Egypt;
| | - Jialiang Peng
- School of Data Science and Technology, Heilongjiang University, Harbin 150080, China;
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt;
| | - Ahmed A. Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt;
- Centre for Excellence in Cybersecurity, Quantum Information Processing, and Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt
- School of Information Technology and Computer Science, Nile University, Giza 12588, Egypt
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31
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Mehmood A, Khan MA, Sharif M, Khan SA, Shaheen M, Saba T, Riaz N, Ashraf I. Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection. MULTIMEDIA TOOLS AND APPLICATIONS 2020; 83:14979-14999. [DOI: 10.1007/s11042-020-08928-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 03/26/2020] [Accepted: 04/07/2020] [Indexed: 08/25/2024]
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