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Darbandi MR, Darbandi M, Darbandi S, Bado I, Hadizadeh M, Khorram Khorshid HR. Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study. Eur J Cancer 2024; 209:114227. [PMID: 39053289 DOI: 10.1016/j.ejca.2024.114227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
This article delves into the potential of artificial intelligence (AI) to enhance early breast cancer (BC) detection for improved treatment outcomes and patient care. Utilizing a multimethod approach comprising literature review and experiments, the study systematically reviewed 310 articles utilizing 30 diverse datasets. Among the techniques assessed, recurrent neural network (RNN) emerged as the most accurate, achieving 98.58 % accuracy, followed by genetic principles (GP), transfer learning (TL), and artificial neural networks (ANNs), with accuracies exceeding 96 %. While conventional machine learning (ML) methods demonstrated accuracies above 90 %, DL techniques outperformed them. Evaluation of BC diagnostic models using the Wisconsin breast cancer dataset (WBCD) highlighted logistic regression (LR) and support vector machine (SVM) as the most accurate predictors, with minimal errors for clinical data. Conversely, decision trees (DT) exhibited higher error rates due to overfitting, emphasizing the importance of algorithm selection for complex datasets. Analysis of ultrasound images underscored the significance of preprocessing, while histopathological image analysis using convolutional neural networks (CNNs) demonstrated robust classification capabilities. These findings underscore the transformative potential of ML and DL in BC diagnosis, offering automated, accurate, and accessible diagnostic tools. Collaboration among stakeholders is crucial for further advancements in BC detection methods.
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Affiliation(s)
| | - Mahsa Darbandi
- Fetal Health Research Center, Hope Generation Foundation, Tehran, Iran.
| | - Sara Darbandi
- Gene Therapy and Regenerative Medicine Research Center, Hope Generation Foundation, Tehran, Iran.
| | - Igor Bado
- Department of Oncological Sciences, Tisch Cancer Institute, New York, USA.
| | - Mohammad Hadizadeh
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hamid Reza Khorram Khorshid
- Genetics Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran; Personalized Medicine and Genometabolics Research Center, Hope Generation Foundation, Tehran, Iran.
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Sun K, Zheng Y, Yang X, Jia W. A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma. Med Biol Eng Comput 2024; 62:1427-1440. [PMID: 38233683 DOI: 10.1007/s11517-023-03004-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024]
Abstract
In recent years, predicting gene mutations on whole slide imaging (WSI) has gained prominence. The primary challenge is extracting global information and achieving unbiased semantic aggregation. To address this challenge, we propose a novel Transformer-based aggregation model, employing a self-learning weight aggregation mechanism to mitigate semantic bias caused by the abundance of features in WSI. Additionally, we adopt a random patch training method, which enhances model learning richness by randomly extracting feature vectors from WSI, thus addressing the issue of limited data. To demonstrate the model's effectiveness in predicting gene mutations, we leverage the lung adenocarcinoma dataset from Shandong Provincial Hospital for prior knowledge learning. Subsequently, we assess TP53, CSMD3, LRP1B, and TTN gene mutations using lung adenocarcinoma tissue pathology images and clinical data from The Cancer Genome Atlas (TCGA). The results indicate a notable increase in the AUC (Area Under the ROC Curve) value, averaging 4%, attesting to the model's performance improvement. Our research offers an efficient model to explore the correlation between pathological image features and molecular characteristics in lung adenocarcinoma patients. This model introduces a novel approach to clinical genetic testing, expected to enhance the efficiency of identifying molecular features and genetic testing in lung adenocarcinoma patients, ultimately providing more accurate and reliable results for related studies.
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Affiliation(s)
- Kai Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China.
| | - Xinbo Yang
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China.
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Ejiyi CJ, Qin Z, Monday H, Ejiyi MB, Ukwuoma C, Ejiyi TU, Agbesi VK, Agu A, Orakwue C. Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation. Biofactors 2024; 50:114-134. [PMID: 37695269 DOI: 10.1002/biof.1995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/18/2023] [Indexed: 09/12/2023]
Abstract
Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)-augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine-learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.
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Affiliation(s)
- Chukwuebuka Joseph Ejiyi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhen Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Happy Monday
- Department of Computer Science, Oxford Brookes University and Chengdu University of Technology of China, Chengdu, China
| | | | - Chiagoziem Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Thomas Ugochukwu Ejiyi
- Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Enugu, Nigeria
| | - Victor Kwaku Agbesi
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Amarachi Agu
- Department of Public Health, University of Nigeria Enugu Campus, Enugu, Nigeria
| | - Chiduzie Orakwue
- Department of Agricultural and Bio-Resources Engineering, College of Engineering Federal University of Agriculture Abeokuta, Nigeria
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Ukwuoma CC, Cai D, Heyat MBB, Bamisile O, Adun H, Al-Huda Z, Al-Antari MA. Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101596. [PMID: 37275558 PMCID: PMC10211254 DOI: 10.1016/j.jksuci.2023.101596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Abstract
COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.
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Affiliation(s)
- Chiagoziem C Ukwuoma
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Dongsheng Cai
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Olusola Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, China
| | - Humphrey Adun
- Department of Mechanical and Energy Systems Engineering, Cyprus International University, Nicosia, North Nicosia, Cyprus
| | - Zaid Al-Huda
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - Mugahed A Al-Antari
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
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Yusoff M, Haryanto T, Suhartanto H, Mustafa WA, Zain JM, Kusmardi K. Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review. Diagnostics (Basel) 2023; 13:diagnostics13040683. [PMID: 36832171 PMCID: PMC9955565 DOI: 10.3390/diagnostics13040683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.
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Affiliation(s)
- Marina Yusoff
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- College of Computing, Informatic and Media, Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- Correspondence: (M.Y.); (W.A.M.)
| | - Toto Haryanto
- Department of Computer Science, IPB University, Bogor 16680, Indonesia
| | - Heru Suhartanto
- Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia
- Correspondence: (M.Y.); (W.A.M.)
| | - Jasni Mohamad Zain
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- College of Computing, Informatic and Media, Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
| | - Kusmardi Kusmardi
- Department of Anatomical Pathology, Faculty of Medicine, Universitas Indonesia/Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia
- Human Cancer Research Cluster, Indonesia Medical Education and Research Institute, Universitas Indonesia, Jakarta 10430, Indonesia
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Sarker MMK, Akram F, Alsharid M, Singh VK, Yasrab R, Elyan E. Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images. Diagnostics (Basel) 2022; 13:103. [PMID: 36611396 PMCID: PMC9818943 DOI: 10.3390/diagnostics13010103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtype classification study. In this paper, we propose a convolutional neural network (CNN)-based breast cancer classification method for hematoxylin and eosin (H&E) whole slide images (WSIs). The proposed method incorporates fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squeeze and excitation (DSE) network to accurately classify breast cancer tissue into binary (benign and malignant) and eight subtypes using histopathology images. For that, a pre-trained EfficientNetV2 network is used as a backbone with a modified DSE block that combines the spatial and channel-wise squeeze and excitation layers to highlight important low-level and high-level abstract features. Our method outperformed ResNet101, InceptionResNetV2, and EfficientNetV2 networks on the publicly available BreakHis dataset for the binary and multi-class breast cancer classification in terms of precision, recall, and F1-score on multiple magnification levels.
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Affiliation(s)
- Md. Mostafa Kamal Sarker
- National Subsea Center, Robert Gordon University, Aberdeen AB10 7AQ, UK
- Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
| | - Farhan Akram
- Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Mohammad Alsharid
- Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | | | - Robail Yasrab
- Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
| | - Eyad Elyan
- School of Computing Science and Digital Media, Robert Gordon University, Aberdeen AB10 7AQ, UK
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Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images. Comput Biol Med 2022; 151:106324. [PMID: 36423531 PMCID: PMC9671873 DOI: 10.1016/j.compbiomed.2022.106324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/27/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.
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Ukwuoma CC, Qin Z, Agbesi VK, Ejiyi CJ, Bamisile O, Chikwendu IA, Tienin BW, Hossin MA. LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images. Comput Biol Med 2022; 150:106195. [PMID: 37859288 PMCID: PMC9561436 DOI: 10.1016/j.compbiomed.2022.106195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/03/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022]
Abstract
According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier research. To achieve the specified goal, we explored the Inception V3 deep artificial neural network. This study proposed LCSB-Inception; a two-path (L and AB channel) Inception V3 network along the first three convolutional layers. The RGB input image is first transformed to CIE LAB coordinates (L channel which is aimed at learning the textural and edge features of the Chest X-Ray and AB channel which is aimed at learning the color variations of the Chest X-ray images). The L achromatic channel and the AB channels filters are set to 50%L-50%AB. This method saves between one-third and one-half of the parameters in the divided branches. We further introduced a global second-order pooling at the last two convolutional blocks for more robust image feature extraction against the conventional max-pooling. The detection accuracy of the LCSB-Inception is further improved by employing the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique on the input image before feeding them to the network. The proposed LCSB-Inception network is experimented on using two loss functions (Categorically smooth loss and categorically Cross-entropy) and two learning rates whereas Accuracy, Precision, Sensitivity, Specificity F1-Score, and AUC Score were used for evaluation via the chestX-ray-15k (Data_1) and COVID-19 Radiography dataset (Data_2). The proposed models produced an acceptable outcome with an accuracy of 0.97867 (Data_1) and 0.98199 (Data_2) according to the experimental findings. In terms of COVID-19 identification, the suggested models outperform conventional deep learning models and other state-of-the-art techniques presented in the literature based on the results.
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Affiliation(s)
- Chiagoziem C Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan, PR China.
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan, PR China.
| | - Victor Kwaku Agbesi
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, PR China
| | - Chukwuebuka J Ejiyi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan, PR China
| | - Olusola Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, Chenghua District, Chengdu, Sichuan, PR China
| | - Ijeoma A Chikwendu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Sichuan, PR China
| | - Bole W Tienin
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Sichuan, PR China
| | - Md Altab Hossin
- School of Innovation and Entrepreneurship, Chengdu University, No. 2025, Chengluo Avenue, 610106, Chengdu, Sichuan, PR China
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