1
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Sasmal B, Dhal KG. A survey on the utilization of Superpixel image for clustering based image segmentation. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-63. [PMID: 37362658 PMCID: PMC9992924 DOI: 10.1007/s11042-023-14861-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 06/28/2023]
Abstract
Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.
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Affiliation(s)
- Buddhadev Sasmal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
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2
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Das A, Namtirtha A, Dutta A. Lévy–Cauchy arithmetic optimization algorithm combined with rough K-means for image segmentation. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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3
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Rao Y, Lv Q, Zeng S, Yi Y, Huang C, Gao Y, Cheng Z, Sun J. COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomed Signal Process Control 2023; 81:104486. [PMID: 36505089 PMCID: PMC9721288 DOI: 10.1016/j.bspc.2022.104486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/23/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
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Affiliation(s)
- Yunbo Rao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qingsong Lv
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shaoning Zeng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, China
| | - Yuling Yi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Cheng Huang
- Fifth Clinical College of Chongqing Medical University, Chongqing, 402177, China
| | - Yun Gao
- Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Zhanglin Cheng
- Advanced Technology Chinese Academy of Sciences, Shenzhen, 610042, China
| | - Jihong Sun
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310014, China
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4
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Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. SENSORS (BASEL, SWITZERLAND) 2023; 23:527. [PMID: 36617124 PMCID: PMC9824505 DOI: 10.3390/s23010527] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
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Affiliation(s)
- Md. Mahadi Hasan
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Muhammad Usama Islam
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Muhammad Jafar Sadeq
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Wai-Keung Fung
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| | - Jasim Uddin
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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5
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Biomedical Image Segmentation Using Fuzzy Artificial Cell Swarm Optimization (FACSO). Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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6
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Jalali SMJ, Ahmadian M, Ahmadian S, Hedjam R, Khosravi A, Nahavandi S. X-ray image based COVID-19 detection using evolutionary deep learning approach. EXPERT SYSTEMS WITH APPLICATIONS 2022; 201:116942. [PMID: 35378906 PMCID: PMC8966159 DOI: 10.1016/j.eswa.2022.116942] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/24/2021] [Accepted: 03/17/2022] [Indexed: 06/01/2023]
Abstract
Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.
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Affiliation(s)
| | - Milad Ahmadian
- Department of Computer Engineering, Razi University, Kermanshah, Iran
| | - Sajad Ahmadian
- Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | - Rachid Hedjam
- Department of Computer science, Sultan Qaboos University, Muscat, Sultanate of Oman
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
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7
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Ding W, Chakraborty S, Mali K, Chatterjee S, Nayak J, Das AK, Banerjee S. An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images. IEEE TRANSACTIONS ON FUZZY SYSTEMS : A PUBLICATION OF THE IEEE NEURAL NETWORKS COUNCIL 2022; 30:2902-2914. [PMID: 36345371 PMCID: PMC9454279 DOI: 10.1109/tfuzz.2021.3097806] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/29/2021] [Accepted: 07/12/2021] [Indexed: 05/04/2023]
Abstract
A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.
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Affiliation(s)
- Weiping Ding
- School of Information Science and TechnologyNantong University Nantong 226019 China
| | - Shouvik Chakraborty
- Department of Computer Science and EngineeringUniversity of Kalyani Kalyani 741235 India
| | - Kalyani Mali
- Department of Computer Science and EngineeringUniversity of Kalyani Kalyani 741235 India
| | - Sankhadeep Chatterjee
- Department of Computer Science and EngineeringUniversity of Engineering and Management Kolkata 700160 India
| | - Janmenjoy Nayak
- Department of Computer Science and EngineeringAditya Institute of Technology and Management Srikakulam 532201 India
| | - Asit Kumar Das
- Department of Computer Science and TechnologyIndian Institute of Engineering Science and Technology Howrah 711103 India
| | - Soumen Banerjee
- Department of Electronics and Communication EngineeringUniversity of Engineering and Management Kolkata 700160 India
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8
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Novel deep learning approach to model and predict the spread of COVID-19. INTELLIGENT SYSTEMS WITH APPLICATIONS 2022; 14. [PMCID: PMC8923717 DOI: 10.1016/j.iswa.2022.200068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM).
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9
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Riaz M, Bashir M, Younas I. Metaheuristics based COVID-19 detection using medical images: A review. Comput Biol Med 2022; 144:105344. [PMID: 35294913 PMCID: PMC8907145 DOI: 10.1016/j.compbiomed.2022.105344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 01/02/2023]
Abstract
Many countries in the world have been facing the rapid spread of COVID-19 since February 2020. There is a dire need for efficient and cheap automated diagnosis systems that can reduce the pressure on healthcare systems. Extensive research is being done on the use of image classification for the detection of COVID-19 through X-ray and CT-scan images of patients. Deep learning has been the most popular technique for image classification during the last decade. However, the performance of deep learning-based methods heavily depends on the architecture of the deep neural network. Over the last few years, metaheuristics have gained popularity for optimizing the architecture of deep neural networks. Metaheuristics have been widely used to solve different complex non-linear optimization problems due to their flexibility, simplicity, and problem independence. This paper aims to study the different image classification techniques for chest images, including the applications of metaheuristics for optimization and feature selection of deep learning and machine learning models. The motivation of this study is to focus on applications of different types of metaheuristics for COVID-19 detection and to shed some light on future challenges in COVID-19 detection from medical images. The aim is to inspire researchers to focus their research on overlooked aspects of COVID-19 detection.
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Affiliation(s)
| | - Maryam Bashir
- FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan.
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10
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Amara K, Aouf A, Kennouche H, Djekoune AO, Zenati N, Kerdjidj O, Ferguene F. COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation. COMPUTERS & GRAPHICS 2022; 104:11-23. [PMID: 35310449 PMCID: PMC8923016 DOI: 10.1016/j.cag.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
With the Coronavirus disease 2019 (COVID-19) spread, causing a world pandemic, and recently, the virus new variants continue to appear, making the situation more challenging and threatening, the visual assessment and quantification by expert radiologists have become costly and error-prone. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the disease spread. In order to assist the medical professionals and reduce workload and the time the COVID-19 diagnosis cycle takes, this paper proposes a novel neural network architecture termed as O-Net to automatically segment chest Computerised Tomography Ct-scans infected by COVID-19 with optimised computing power and memory occupation. The O-Net consists of two convolutional autoencoders with an upsampling channel and a downsampling channel. Experimental tests show our proposal's effectiveness and potential, with a dice score of 0.86, pixel accuracy, precision, specificity of 0.99, 0.99, 0.98, respectively. Performance on the external dataset illustrates generalisation and scalability capabilities of the O-Net model to Ct-scan obtained from different scanners with different sizes. The second objective of this work is to introduce our virtual reality platform, COVIR, that visualises and manipulates 3D reconstructed lungs and segmented infected lesions caused by COVID-19. COVIR platform acts as a reading and visualisation support for medical practitioners to diagnose COVID-19 lung infection. The COVIR platform could be used for medical education professional practice and training. It was tested by Thirteen participants (medical staff, researchers, and collaborators), they conclude that the 3D VR visualisation of segmented Ct-Scan provides an aid diagnosis tool for better interpretation.
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Affiliation(s)
- Kahina Amara
- CDTA Centre for Development of Advanced Technologies, City 20 August 1956 Baba Hassen, Algiers, Algeria
| | - Ali Aouf
- USTHB University of science and technology Houari Boumediene, B.P 32 El Alia 16111 Bab Ezzouar, Algiers, Algeria
| | - Hoceine Kennouche
- USTHB University of science and technology Houari Boumediene, B.P 32 El Alia 16111 Bab Ezzouar, Algiers, Algeria
| | - A Oualid Djekoune
- CDTA Centre for Development of Advanced Technologies, City 20 August 1956 Baba Hassen, Algiers, Algeria
| | - Nadia Zenati
- CDTA Centre for Development of Advanced Technologies, City 20 August 1956 Baba Hassen, Algiers, Algeria
| | - Oussama Kerdjidj
- CDTA Centre for Development of Advanced Technologies, City 20 August 1956 Baba Hassen, Algiers, Algeria
| | - Farid Ferguene
- USTHB University of science and technology Houari Boumediene, B.P 32 El Alia 16111 Bab Ezzouar, Algiers, Algeria
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11
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Karthik R, Menaka R, M H, Won D. Contour-enhanced attention CNN for CT-based COVID-19 segmentation. PATTERN RECOGNITION 2022; 125:108538. [PMID: 35068591 PMCID: PMC8767763 DOI: 10.1016/j.patcog.2022.108538] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 09/14/2021] [Accepted: 01/14/2022] [Indexed: 05/14/2023]
Abstract
Accurate detection of COVID-19 is one of the challenging research topics in today's healthcare sector to control the coronavirus pandemic. Automatic data-powered insights for COVID-19 localization from medical imaging modality like chest CT scan tremendously augment clinical care assistance. In this research, a Contour-aware Attention Decoder CNN has been proposed to precisely segment COVID-19 infected tissues in a very effective way. It introduces a novel attention scheme to extract boundary, shape cues from CT contours and leverage these features in refining the infected areas. For every decoded pixel, the attention module harvests contextual information in its spatial neighborhood from the contour feature maps. As a result of incorporating such rich structural details into decoding via dense attention, the CNN is able to capture even intricate morphological details. The decoder is also augmented with a Cross Context Attention Fusion Upsampling to robustly reconstruct deep semantic features back to high-resolution segmentation map. It employs a novel pixel-precise attention model that draws relevant encoder features to aid in effective upsampling. The proposed CNN was evaluated on 3D scans from MosMedData and Jun Ma benchmarked datasets. It achieved state-of-the-art performance with a high dice similarity coefficient of 85.43% and a recall of 88.10%.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems (CCPS), Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems (CCPS), Vellore Institute of Technology, Chennai, India
| | - Hariharan M
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - Daehan Won
- System Sciences and Industrial Engineering, Binghamton University, United States
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12
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Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search. MATHEMATICS 2021. [DOI: 10.3390/math9192383] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics.
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13
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Chakraborty S, Mali K. SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation. EXPERT SYSTEMS WITH APPLICATIONS 2021; 178:115069. [PMID: 33897121 PMCID: PMC8055948 DOI: 10.1016/j.eswa.2021.115069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/05/2021] [Accepted: 04/16/2021] [Indexed: 05/21/2023]
Abstract
The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.
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Affiliation(s)
- Shouvik Chakraborty
- Department of Computer Science and Engineering, University of Kalyani, India
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, India
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14
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Alam ST, Ahmed S, Ali SM, Sarker S, Kabir G, Ul-Islam A. Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2021; 239:108193. [PMID: 34121813 PMCID: PMC8184405 DOI: 10.1016/j.ijpe.2021.108193] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/23/2021] [Accepted: 05/29/2021] [Indexed: 05/07/2023]
Abstract
The COVID-19 outbreak has demonstrated the diverse challenges that supply chains face to significant disruptions. Vaccine supply chains are no exception. Therefore, it is elemental that challenges to the COVID-19 vaccine supply chain (VSC) are identified and prioritized to pave the way out of this pandemic. This study combines the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy sets (IFS) to explore the key challenges of the COVID-19 VSC. The IFS theory tackles the uncertainty of key challenges while DEMATEL addresses the interlaced causal relationships among crucial challenges to the COVID-19 VSC. This work identifies 15 challenges and reveals that 'Limited number of vaccine manufacturing companies', 'Inappropriate coordination with local organizations', 'Lack of vaccine monitoring bodies', 'Difficulties in monitoring and controlling vaccine temperature', and 'Vaccination cost and lack of financial support for vaccine purchase' are the most critical challenges. The causal interactions along with mutual relationships among these challenges are also scrutinized, and implications for sustainable development goals (SDGs) are drawn. The results offer practical guidelines for stakeholders and government policy makers around the world to develop an improved VSC for the COVID-19 virus.
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Affiliation(s)
- Shahriar Tanvir Alam
- Department of Industrial and Production Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh
| | - Sayem Ahmed
- Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh
| | - Syed Mithun Ali
- Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - Sudipa Sarker
- Department of Business Development and Technology, Aarhus University, Denmark
| | - Golam Kabir
- Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, Canada
| | - Asif Ul-Islam
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur, Bangladesh
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15
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Ghaderzadeh M, Aria M, Asadi F. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9942873. [PMID: 34458373 PMCID: PMC8390162 DOI: 10.1155/2021/9942873] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/16/2021] [Accepted: 08/04/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases. METHOD This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity. RESULT The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19. CONCLUSION X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Student Research Committee, Department and Faculty of Health Information Technology and Ma School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrad Aria
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. SENSORS 2021; 21:s21062215. [PMID: 33810066 PMCID: PMC8004971 DOI: 10.3390/s21062215] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/14/2021] [Accepted: 03/18/2021] [Indexed: 12/13/2022]
Abstract
Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 ± 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 ± 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).
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Affiliation(s)
- Athanasios Voulodimos
- Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece;
- Correspondence:
| | - Eftychios Protopapadakis
- Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece;
| | - Iason Katsamenis
- School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Athens, Greece; (I.K.); (A.D.); (N.D.)
| | - Anastasios Doulamis
- School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Athens, Greece; (I.K.); (A.D.); (N.D.)
| | - Nikolaos Doulamis
- School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Athens, Greece; (I.K.); (A.D.); (N.D.)
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