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Bilal A, Imran A, Liu X, Liu X, Ahmad Z, Shafiq M, El-Sherbeeny AM, Long H. BC-QNet: A quantum-infused ELM model for breast cancer diagnosis. Comput Biol Med 2024; 175:108483. [PMID: 38704900 DOI: 10.1016/j.compbiomed.2024.108483] [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: 02/12/2024] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Xiling Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Zohaib Ahmad
- Department of Criminology & Forensic Sciences Technology, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Yunnan, China
| | - Ahmed M El-Sherbeeny
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
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2
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Bilal A, Imran A, Baig TI, Liu X, Abouel Nasr E, Long H. Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization. Sci Rep 2024; 14:10714. [PMID: 38730250 PMCID: PMC11087531 DOI: 10.1038/s41598-024-61322-w] [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: 01/03/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Talha Imtiaz Baig
- School of Life Science and Technology, University of Electronic Science and Technology of China UESTC, Chengdu, Sichuan, China
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Emad Abouel Nasr
- Industrial Engineering Department, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
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3
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Khanna M, Singh LK, Shrivastava K, Singh R. An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study. Heliyon 2024; 10:e26799. [PMID: 38463826 PMCID: PMC10920178 DOI: 10.1016/j.heliyon.2024.e26799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 01/15/2024] [Accepted: 02/20/2024] [Indexed: 03/12/2024] Open
Abstract
Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.
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Affiliation(s)
- Munish Khanna
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Gautam Buddh Nagar, India
| | - Law Kumar Singh
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Kapil Shrivastava
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Rekha Singh
- Department of Physics, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, Uttar Pradesh, India
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4
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Saleh GA, Batouty NM, Gamal A, Elnakib A, Hamdy O, Sharafeldeen A, Mahmoud A, Ghazal M, Yousaf J, Alhalabi M, AbouEleneen A, Tolba AE, Elmougy S, Contractor S, El-Baz A. Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review. Cancers (Basel) 2023; 15:5216. [PMID: 37958390 PMCID: PMC10650187 DOI: 10.3390/cancers15215216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
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Affiliation(s)
- Gehad A. Saleh
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Nihal M. Batouty
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Abdelrahman Gamal
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elnakib
- Electrical and Computer Engineering Department, School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA;
| | - Omar Hamdy
- Surgical Oncology Department, Oncology Centre, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Marah Alhalabi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Amal AbouEleneen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elsaid Tolba
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
- The Higher Institute of Engineering and Automotive Technology and Energy, New Heliopolis, Cairo 11829, Egypt
| | - Samir Elmougy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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5
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Reshan MSA, Amin S, Zeb MA, Sulaiman A, Alshahrani H, Azar AT, Shaikh A. Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques. Life (Basel) 2023; 13:2093. [PMID: 37895474 PMCID: PMC10608611 DOI: 10.3390/life13102093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.
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Affiliation(s)
- Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (M.S.A.R.); (A.S.)
| | - Samina Amin
- Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan; (S.A.); (M.A.Z.)
| | - Muhammad Ali Zeb
- Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan; (S.A.); (M.A.Z.)
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (A.S.); (H.A.)
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (A.S.); (H.A.)
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (M.S.A.R.); (A.S.)
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6
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Suresh T, Brijet Z, Subha TD. Imbalanced medical disease dataset classification using enhanced generative adversarial network. Comput Methods Biomech Biomed Engin 2023; 26:1702-1718. [PMID: 36322625 DOI: 10.1080/10255842.2022.2134729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 09/17/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022]
Abstract
In general, the imbalanced dataset is a major issue in health applications. The medical data classification faces the imbalanced count of data samples, here at least one class forms only a very small minority of the data, but it is a drawback of most of the machine learning algorithms. The medical datasets are mostly imbalanced in its class labels. When the dataset is imbalanced, the existing classification algorithms typically perform badly on minority class cases. To deal the class imbalance issue, an enhanced generative adversarial network (E-GAN) is proposed in this article. The proposed approach is the consolidation of deep convolutional generative adversarial network and modified convolutional neural network (DCG-MCNN). Initially, the imbalanced data is converted into balanced data in pre-processing process. Data preprocessing comprise of data cleaning, data normalization, data transformation and data reduction using Radius Synthetic minority oversampling technique (RSMOTE) method. The DCG is considered for balancing the dataset generating extra samples under training dataset. This training dataset based, the medical disease classification is enhanced by modified CNN diagnosis model. The proposed system performed is executed in MATLAB. The performance analysis is implemented under the Breast Cancer Wisconsin Dataset that provides the higher maximum geometry mean (MGM) of 8.686, 2.931 and 5.413%, and higher Matthews's correlation coefficient (MCC) of 9.776, 1.841 and 5.413% compared to the existing methods.
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Affiliation(s)
- T Suresh
- Department of Electronics and Communication Engineering, R.M.K. Engineering college, Kavaraipettai, Tamil Nadu, India
| | - Z Brijet
- Department of Electronics and Instrumentation Engineering, Velammal Engineering College, Surapet, Chennai, Tamil Nadu, India
| | - T D Subha
- Department of Electronics and Communication Engineering, R.M.K. Engineering college, Kavaraipettai, Tamil Nadu, India
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7
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Ajlouni N, Özyavaş A, Takaoğlu M, Takaoğlu F, Ajlouni F. Medical image diagnosis based on adaptive Hybrid Quantum CNN. BMC Med Imaging 2023; 23:126. [PMID: 37710188 PMCID: PMC10500912 DOI: 10.1186/s12880-023-01084-5] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
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Affiliation(s)
- Naim Ajlouni
- Faculty of Engineering, Istanbul Atlas University, 34295, Istanbul, Türkiye.
- Faculty of Engineering, Istanbul Atlas University, Hamidiye, Anadolu Cd. No:40, 34408, 34403, Kağıthane, Istanbul, Turkey.
- Tübitak Bilgem, Barış, 1802. Sk. No:1, 41400, Gebze, Kocaeli, Turkey.
- Lancashire College of Further Education, Appleby Street, Lancashire, BB1 3BL, Blackburn, UK.
| | - Adem Özyavaş
- Faculty of Engineering, Istanbul Atlas University, 34295, Istanbul, Türkiye
| | - Mustafa Takaoğlu
- The Scientific and Technological Research Council of Türkiye (TÜBİTAK), BİLGEM, Kocaeli, Türkiye
| | - Faruk Takaoğlu
- The Scientific and Technological Research Council of Türkiye (TÜBİTAK), BİLGEM, Kocaeli, Türkiye
| | - Firas Ajlouni
- Department of Computer Science, Lancashire College of Further Education, Accrington, BB5 OHJ, UK
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8
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Yeganeh A, Shadman A, Shongwe SC, Abbasi SA. Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08257-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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9
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Nasser M, Yusof UK. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction. Diagnostics (Basel) 2023; 13:diagnostics13010161. [PMID: 36611453 PMCID: PMC9818155 DOI: 10.3390/diagnostics13010161] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
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10
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Exploring different computational approaches for effective diagnosis of breast cancer. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 177:141-150. [PMID: 36509230 DOI: 10.1016/j.pbiomolbio.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/28/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022]
Abstract
Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.
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11
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A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07290-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif Intell Med 2022; 127:102276. [DOI: 10.1016/j.artmed.2022.102276] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 10/18/2021] [Accepted: 03/04/2022] [Indexed: 02/07/2023]
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13
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Mahour S, Kumar Verma S, Kumar Arora J, Srivastava S. Carboxyl appended polymerized seed composite with controlled structural properties for enhanced heavy metal capture. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.120247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Alkhathlan L, Saudagar AKJ. Predicting and Classifying Breast Cancer Using Machine Learning. J Comput Biol 2021; 29:497-514. [PMID: 34883032 DOI: 10.1089/cmb.2021.0236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.
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Affiliation(s)
- Lina Alkhathlan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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15
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Mridha MF, Hamid MA, Monowar MM, Keya AJ, Ohi AQ, Islam MR, Kim JM. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis. Cancers (Basel) 2021; 13:6116. [PMID: 34885225 PMCID: PMC8656730 DOI: 10.3390/cancers13236116] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022] Open
Abstract
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.
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Affiliation(s)
- Muhammad Firoz Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Abdul Hamid
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Muhammad Mostafa Monowar
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Ashfia Jannat Keya
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Abu Quwsar Ohi
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh;
| | - Jong-Myon Kim
- Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea
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Abstract
Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.
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Sethy PK, Pandey C, Khan MR, Behera SK, Vijaykumar K, Panigrahi S. A cost-effective computer-vision based breast cancer diagnosis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189848] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.
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Affiliation(s)
| | - Chanki Pandey
- Department of ET&T Engineering, Government Engineering College, Jagdalpur, CG, India
| | - Mohammad Rafique Khan
- Department of ET&T Engineering, Government Engineering College, Jagdalpur, CG, India
| | | | - K. Vijaykumar
- Department of Computer Science & Engineering, St. Joseph’s Institute of Technology, India
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19
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Ramesh Dhanaseelan F, Jeya Sutha M. Detection of Breast Cancer Based on Fuzzy Frequent Itemsets Mining. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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20
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Jafari-Marandi R. Supervised or unsupervised learning? Investigating the role of pattern recognition assumptions in the success of binary predictive prescriptions. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.12.063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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21
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Rahman MA, Muniyandi RC, Albashish D, Rahman MM, Usman OL. Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer. PeerJ Comput Sci 2021; 7:e344. [PMID: 33816995 PMCID: PMC7924699 DOI: 10.7717/peerj-cs.344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/30/2020] [Indexed: 05/31/2023]
Abstract
Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.
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Affiliation(s)
- Md Akizur Rahman
- Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Ravie chandren Muniyandi
- Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Dheeb Albashish
- Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, Jordan
| | - Md Mokhlesur Rahman
- Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Opeyemi Lateef Usman
- Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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22
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Guo X, Wang J. Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks. JOURNAL OF HAZARDOUS MATERIALS 2021; 402:123709. [PMID: 33254753 DOI: 10.1016/j.jhazmat.2020.123709] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 07/26/2020] [Accepted: 08/12/2020] [Indexed: 05/22/2023]
Abstract
Microplastics pollution and their interaction with heavy metal ions have gained global concern. It is essential to develop models to predict the sorption capacity of heavy metal ions onto microplastics in global aquatic environments, and to connect the laboratory study results with the field measurement results. In this paper, the artificial neural networks (ANN) models were established based on literature data. for The results showed that the ANN model could predict the sorption capacity of heavy metal ions (including Cd, Pb, Cr, Cu, and Zn) onto microplastics in the global environments with high correlation coefficient (R) values (0.926∼0.994). The predicted sorption capacity was influenced by the initial concentration of heavy metal ions and the salinity in surrounding water. The predicted sorption capacity in rivers and lakes was higher than that in the ocean. Aged microplastics had higher affinity to heavy metal ions than virgin microplastics. The predicted sorption capacity of Cd, Pb, and Zn ions onto large microplastics (5 mm) was less than 0.12 μg/g. The predicted amount was in agreement with the field measurement results, suggesting that the laboratory studies can provide useful information for projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments.
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Affiliation(s)
- Xuan Guo
- Collaborative Innovation Center for Advanced Nuclear Energy Technology, INET, Tsinghua University, Beijing 100084, China
| | - Jianlong Wang
- Collaborative Innovation Center for Advanced Nuclear Energy Technology, INET, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Radioactive Waste Treatment, INET, Tsinghua University, Beijing 100084, China.
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ElOuassif B, Idri A, Hosni M, Abran A. Classification techniques in breast cancer diagnosis: A systematic literature review. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2020.1811159] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Bouchra ElOuassif
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Ali Idri
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Mohamed Hosni
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Alain Abran
- Department of Software Engineering and Information Technology, Ecole De Technologie Supérieure, –university of Québec, Montreal, Canada
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Supriya M, Deepa AJ. A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment. Health Care Manag Sci 2020; 23:414-426. [PMID: 31686276 DOI: 10.1007/s10729-019-09498-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 09/20/2019] [Indexed: 10/25/2022]
Abstract
Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions concentrated on the prediction of BC. The prevailing prediction model is time-consuming and have less accuracy. To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN). Primarily, the unprocessed BC data are regarded as the input. The big data (BD) storage comprises some repeated information. Secondarily, such repeated data are eliminated by utilizing Hadoop MapReduce. In the subsequent stage, the data are preprocessed utilizing replacing of missing attributes (RMA) and normalization techniques. Subsequently, the features are generally chosen by utilizing Modified Dragonfly algorithm (MDF). Then, the selected features are inputted for classification. Here, it classifies the features utilizing OANN. Optimization is done by employing the Gray Wolf Optimization (GWO) algorithm. Experiential outcomes are contrasted with prevailing IWDT (Improved Weighted-Decision Tree) in respect of precision, recall, accuracy, and ROC.
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Affiliation(s)
| | - A J Deepa
- Ponjesly College of Engineering, Kaniyakumari, India
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25
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Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04850-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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26
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Khayat Kashani HR, Azhari S, Nayebaghayee H, Salimi S, Mohammadi HR. Prediction value of preoperative findings on meningioma grading using artificial neural network. Clin Neurol Neurosurg 2020; 196:105947. [PMID: 32521393 DOI: 10.1016/j.clineuro.2020.105947] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/17/2020] [Accepted: 05/18/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Meningioma is the most common brain tumor in adults. Grade 1 meningiomas have excellent prognoses, but grades 2 and 3 usually have worse outcomes, higher recurrence rates, and higher mortality rates. Preoperative determination of tumor grade may be helpful in deciding the type of surgery and the rate of resection. Blood markers have been used to predict the rate of malignancy and prognosis of tumors in different regions, including the brain. The current study investigated the use of blood markers on predicting meningioma grade. PATIENTS AND METHODS Patients with newly diagnosed meningiomas were retrospectively reviewed. Data on the patients' demographics, tumor locations, blood markers, and tumor pathology grades was extracted. The relationship between preoperative findings and tumor grade was statistically analyzed, and using the same findings and an artificial neural network, the accuracy of tumor grade prediction was evaluated. RESULTS This study included 95 patients, 69 cases (72.4 %) of grade 1, 23 cases of grade 2 (24.4 %) and 3 cases of grade 3 (3.2 %) meningiomas. Monocyte and neutrophil counts as well as lymphocyte-to-monocyte ratio (LMR) were significantly different between low grade and high grade meningiomas, with higher monocyte and neutrophil counts and higher LMR associated with high grade meningiomas (p < 0.05). Evaluation of the data with an artificial neural network using RBF with 5 variables (age, monocyte count, LMR, platelet-to-lymphocyte ratio (PLR), and neutrophil count) indicated that tumor grade can be determined with 83 % accuracy using an artificial neural network. CONCLUSION A preoperative high monocyte count and high LMR are associated with high grade meningioma. An artificial neural network using preoperative data can acceptably be used to characterize meningioma tumor grades.
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Affiliation(s)
- Hamid Reza Khayat Kashani
- Department of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Shirzad Azhari
- Department of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein Nayebaghayee
- Department of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Salimi
- Clinical Research and Development Unit, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Development Unit, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hasan Reza Mohammadi
- Department of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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27
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Yavuz E, Eyupoglu C. An effective approach for breast cancer diagnosis based on routine blood analysis features. Med Biol Eng Comput 2020; 58:1583-1601. [PMID: 32436139 DOI: 10.1007/s11517-020-02187-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 05/01/2020] [Indexed: 12/24/2022]
Abstract
Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstract.
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Affiliation(s)
- Erdem Yavuz
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Yildirim, Bursa, Turkey.
| | - Can Eyupoglu
- Department of Computer Engineering, Air Force Academy, National Defence University, Yesilyurt, Istanbul, Turkey
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28
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Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies. ELECTRONICS 2020. [DOI: 10.3390/electronics9050796] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle.
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Antwi P, Zhang D, Xiao L, Kabutey FT, Quashie FK, Luo W, Meng J, Li J. Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 690:108-120. [PMID: 31284185 DOI: 10.1016/j.scitotenv.2019.06.530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/29/2019] [Accepted: 06/30/2019] [Indexed: 06/09/2023]
Abstract
Two novel feedforward backpropagation Artificial Neural Networks (ANN)-based-models (8:NH:1 and 7:NH:1) combined with Box-Behnken design of experiments methodology was proposed and developed to model NH4+ and Total Nitrogen (TN) removal within an upflow-sludge-bed (USB) reactor treating nitrogen-rich wastewater via Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process. ANN were developed by optimizing network architecture parameters via response surface methodology. Based on the goodness-of-fit standards, the proposed three-layered NH4+ and TN removal ANN-based-models trained with Levenberg-Marquardt-algorithm demonstrated high-performance as computations exhibited smaller deviations-(±2.1%) as well as satisfactory coefficient of determination (R2), fractional variance-(FV), and index of agreement-(IA) ranging 0.989-0.997, 0.003-0.031 and 0.993-0.998, respectively. The computational results affirmed that the ANN architecture which was optimized with response surface methodology enhanced the efficiency of the ANN-based-models. Furthermore, the overall performance of the developed ANN-based models revealed that modeling intricate biological systems (such as SNAP) using ANN-based models with the view to improve removal efficiencies, establish process control strategies and optimize performance is highly feasible. Microbial community analysis conducted with 16S rRNA high-throughput approach revealed that Candidatus Kuenenia was the most pronounced genera which accounted for 13.11% followed by Nitrosomonas-(6.23%) and Proteocatella-(3.1%), an indication that nitrogen removal pathway within the USB was mainly via partial-nitritation/anammox process.
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Affiliation(s)
- Philip Antwi
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Dachao Zhang
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Longwen Xiao
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Felix Tetteh Kabutey
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China
| | - Frank Koblah Quashie
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China
| | - Wuhui Luo
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China
| | - Jia Meng
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China; University of Queensland, Advanced Water Management Centre, Gehrman Building, Research Road, The St Lucia, Brisbane, QLD 4072, Australia
| | - Jianzheng Li
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China
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Kadam VJ, Jadhav SM, Vijayakumar K. Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression. J Med Syst 2019; 43:263. [PMID: 31270634 DOI: 10.1007/s10916-019-1397-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 06/19/2019] [Indexed: 11/30/2022]
Abstract
Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be cured. Many researchers proposed numerous methods for early prediction of this Cancer. In this paper, we proposed feature ensemble learning based on Sparse Autoencoders and Softmax Regression for classification of Breast Cancer into benign (non-cancerous) and malignant (cancerous). We used Breast Cancer Wisconsin (Diagnostic) medical data sets from the UCI machine learning repository. The proposed method is assessed using various performance indices like true classification accuracy, specificity, sensitivity, recall, precision, f measure, and MCC. Simulation and result proved that the proposed approach gives better results in terms of different parameters. The prediction results obtained by the proposed approach were very promising (98.60% true accuracy). In addition, the proposed method outperforms the Stacked Sparse Autoencoders and Softmax Regression based (SSAE-SM) model and other State-of-the-art classifiers in terms of various performance indices. Experimental simulations, empirical results, and statistical analyses are also showing that the proposed model is an efficient and beneficial model for classification of Breast Cancer. It is also comparable with the existing machine learning and soft computing approaches present in the related literature.
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Affiliation(s)
- Vinod Jagannath Kadam
- Department of Information Technology, Dr. Babashaeb Ambedkar Technological University, Lonere, India.
| | | | - K Vijayakumar
- Department of Computer Science & Engineering, St. Joseph's Institute of Technology, Chennai, India
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