Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Use of ant colony optimisation algorithms and artificial neural networks optimised by genetic algorithms enables effective detection of anomalies within microalgae populations and prediction of environmental stress effects based on image analysis.
“The use of ant colony optimisation (ACO) algorithms and artificial neural networks optimised by genetic algorithms enables the effective detection of anomalies within microalgae populations and the prediction of environmental stress effects based on image analysis [244].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [244]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Guo, Z., Xu, L., Si, Y., & Razmjooy, N. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22608 · Cited by 73 · see citing works
RetractedSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 20,065 · see citing works
Quantum Mechanics Helps in Searching for a Needle in a Haystack
Grover, L. K. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.325 · Cited by 4,306 · cited 1× · see citing works
“QC typically utilizes parallel quantum processing and probabilistic ways to represent quantum data, including the Grover searching algorithm, which decreases the search time within an organized database with N items to O √ N 22 .”
MethodQuantum-inspired evolutionary algorithm for a class of combinatorial optimization
Han, K.-H., & Kim, J.-H. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2002.804320 · Cited by 1,601 · see citing works
Epidemiological characteristics of and risk factors for breast cancer in the world
Momenimovahed, Z., & Salehiniya, H. (2019) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s176070 · Cited by 1,059 · see citing works
Zheng, B., Yoon, S. W., & Lam, S. S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.044 · Cited by 549 · see citing works
An expert system for detection of breast cancer based on association rules and neural network
Karabatak, M., & İnce, M. C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.02.064 · Cited by 496 · see citing works
Breast cancer classification using deep belief networks
Abdel-Zaher, A. M., & Eldeib, A. M. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.015 · Cited by 492 · see citing works
Deep Convolutional Neural Networks for breast cancer screening
Chougrad, H., Zouaki, H., & Alheyane, O. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.011 · Cited by 470 · see citing works
A quantum-inspired classical algorithm for recommendation systems
Tang, E. (2019) · Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing · doi:10.1145/3313276.3316310 · Cited by 360 · see citing works
McMahon, D. (2007) · Wiley eBooks · doi:10.1002/9780470181386 · Cited by 328 · see citing works
Artificial algae algorithm (AAA) for nonlinear global optimization
Uymaz, S. A., Tezel, G., & Yel, E. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.03.003 · Cited by 271 · see citing works
Bilal, A., Zhu, L., Deng, A., Lu, H., & Wu, N. (2022) · Symmetry · doi:10.3390/sym14071427 · Cited by 206 · see citing works
Self-citationWBCD breast cancer database classification applying artificial metaplasticity neural network
Marcano-Cedeño, A., Quintanilla-Domínguez, J., & Andina, D. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.01.167 · Cited by 191 · see citing works
A novel quantum inspired cuckoo search for knapsack problems
Layeb, A. (2011) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2011.042260 · Cited by 181 · see citing works
Facing classification problems with Particle Swarm Optimization
Falco, I. D., Cioppa, A. D., & Tarantino, E. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2005.09.004 · Cited by 169 · see citing works
Muduli, D., Dash, R., & Majhi, B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101912 · Cited by 164 · see citing works
Sheikhpour, R., Sarram, M. A., & Sheikhpour, R. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.10.005 · Cited by 161 · see citing works
El‐kenawy, E. M., Mirjalili, S., Ibrahim, A. F., Alrahmawy, M. F., El-Said, M., Zaki, R. M., & Eid, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3061058 · Cited by 149 · see citing works
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database
Bilal, A., Sun, G., Li, Y., Mazhar, S., & Khan, A. Q. (2021) · IEEE Access · doi:10.1109/access.2021.3056186 · Cited by 142 · see citing works
Self-citationAnalysis of Breast Cancer Detection Using Different Machine Learning Techniques
Mohammed, S. A., Darrab, S., Noaman, S. A., et al. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-7205-0_10 · Cited by 140 · see citing works
TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection
Ullah, W., Hussain, T., Ullah, F. U. M., Lee, M. Y., & Baik, S. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106173 · Cited by 128 · see citing works
Bezdan, T., Zivkovic, M. Z., Tuba, E., Strumberger, I., Bačanin, N., & Tuba, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51156-2_111 · Cited by 122 · see citing works
Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem
Srikanth, K., Panwar, L. K., Panigrahi, B., et al. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.07.023 · Cited by 114 · see citing works
A parasitic metric learning net for breast mass classification based on mammography
Jiao, Z., Gao, X., Wang, Y., & Li, J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.07.008 · Cited by 108 · see citing works
Bačanin, N., Zivkovic, M. Z., Al-Turjman, F. M., Venkatachalam, K., Trojovský, P., Strumberger, I., & Bezdan, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-09744-2 · Cited by 104 · see citing works
Taghizadeh, E., Heydarheydari, S., Saberi, A., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04965-8 · Cited by 104 · see citing works
Zouache, D., Nouioua, F., & Moussaouı, A. (2015) · Soft Computing · doi:10.1007/s00500-015-1681-x · Cited by 99 · see citing works
A tribe competition-based genetic algorithm for feature selection in pattern classification
Ma, B., & Xia, Y. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.04.042 · Cited by 99 · see citing works
Breast Cancer Prediction system
Kumari, M., & Singh, V. P. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.197 · Cited by 93 · see citing works
A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images
Bilal, A., Sun, G., Mazhar, S., Imran, A., & Latif, J. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2021.2021111 · Cited by 93 · see citing works
Self-citationA Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support
Chen, T., Shang, C., Su, P., Keravnou, E., Zhao, Y., Antoniou, G., & Shen, Q. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101986 · Cited by 82 · see citing works
A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis
Lu, H., Wang, H., & Yoon, S. W. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.040 · Cited by 74 · see citing works
Deep learning algorithm for breast masses classification in mammograms
Gnanasekaran, V. S., Joypaul, S., Sundaram, P. M., & Chairman, D. D. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0070 · Cited by 74 · see citing works
An immune-inspired semi-supervised algorithm for breast cancer diagnosis
Peng, L., Chen, W., Zhou, W., Li, F., Yang, J., & Zhang, J. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.07.020 · Cited by 69 · see citing works
Rehman, K. U., Li, J., Pei, Y., et al. (2021) · Sensors · doi:10.3390/s21144854 · Cited by 68 · see citing works
Survey on recent developments in automatic detection of diabetic retinopathy
Bilal, A., Sun, G., & Mazhar, S. (2021) · Journal Français d Ophtalmologie · doi:10.1016/j.jfo.2020.08.009 · Cited by 67 · see citing works
Self-citationBilal, A., Shafiq, M., Fang, F., Waqar, M., Ullah, I., Ghadi, Y. Y., Long, H., & Zeng, R. (2022) · Sensors · doi:10.3390/s22249603 · Cited by 66 · see citing works
Self-citationDesigning rule-based fuzzy systems for classification in medicine
Pota, M., Esposito, M., & Pietro, G. D. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.03.006 · Cited by 57 · see citing works
Ed-daoudy, A., & Maalmi, K. (2020) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-020-00237-8 · Cited by 56 · see citing works
Bilal, A., Sun, G., Mazhar, S., et al. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-9605-3_1 · Cited by 53 · see citing works
Self-citationNayak, S. K., Rout, P. K., Jagadev, A. K., et al. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.08.001 · Cited by 51 · see citing works
Smooth group L1/2 regularization for input layer of feedforward neural networks
Li, F., Żurada, J. M., & Wu, W. H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.046 · Cited by 50 · see citing works
Correntropy-based robust multilayer extreme learning machines
Chen, L., Honeiné, P., Qu, H., Zhao, J., & Sun, X. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.011 · Cited by 48 · see citing works
SVM Classifier Based Feature Selection Using GA, ACO and PSO for siRNA Design
Prasad, Y., Biswas, K. K., & Jain, C. K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13498-2_40 · Cited by 47 · see citing works
Vision transformer attention with multi-reservoir echo state network for anomaly recognition
Ullah, W., Hussain, T., & Baik, S. W. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103289 · Cited by 47 · see citing works
Combination of loss functions for robust breast cancer prediction
Hajiabadi, H., Babaiyan, V., Zabihzadeh, D., & Hajiabadi, M. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106624 · Cited by 46 · see citing works
Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN
Bilal, A., Sun, G., Li, Y., et al. (2022) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2021.2012525 · Cited by 46 · see citing works
Self-citationOyelade, O. N., & Ezugwu, A. E.-S. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6629 · Cited by 45 · see citing works
Mammography Image Quality Assurance Using Deep Learning
Kretz, T., Mueller, K.-R., Schaeffter, T., & Elster, C. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2983539 · Cited by 43 · see citing works
Shen, L., He, M., Shen, N., Yousefi, N., Wang, C., & Liu, G. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101953 · Cited by 39 · see citing works
Graph based skill acquisition and transfer Learning for continuous reinforcement learning domains
Shoeleh, F., & Asadpour, M. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.08.009 · Cited by 37 · see citing works
Diabetic Retinopathy detection using Weighted Filters and Classification using CNN
Bilal, A., Sun, G., & Mazhar, S. (2021) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit51480.2021.9498466 · Cited by 36 · see citing works
Self-citationHayashi, Y., & Nakano, S. (2015) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2015.12.002 · Cited by 34 · see citing works
Neuro-optimized numerical treatment of HIV infection model
Bilal, A., Sun, G., Mazhar, S., et al. (2021) · International Journal of Biomathematics · doi:10.1142/s1793524521500339 · Cited by 34 · see citing works
Self-citationLikelihood-fuzzy analysis: From data, through statistics, to interpretable fuzzy classifiers
Pota, M., Esposito, M., & Pietro, G. D. (2017) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2017.10.022 · Cited by 26 · see citing works
Sparse pseudoinverse incremental extreme learning machine
Kassani, P. H., Teoh, A. B. J., & Kim, E. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.087 · Cited by 23 · see citing works
Breast Embryology, Anatomy, and Physiology
López, M. E., & Olutoye, O. O. (2017) · Springer eBooks · doi:10.1007/978-3-662-54256-9_27 · Cited by 20 · see citing works
ST-ONCODIAG: A semantic rule-base approach to diagnosing breast cancer base on Wisconsin datasets
Oyelade, O. N., Obiniyi, A. A., Junaidu, S. B., et al. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.12.008 · Cited by 20 · see citing works
Hamed, H. N. A., Kasabov, N. K., & Mariyam, S. (2011) · InTech eBooks · doi:10.5772/10545 · Cited by 18 · see citing works
Ahmed, H. M., Youssef, B. A. B., Elkorany, A. S., Elsharkawy, Z. F., Saleeb, A. A., & El‐Samie, F. E. A. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07876-8 · Cited by 17 · see citing works
Splicing sites prediction of human genome using machine learning techniques
Ullah, W., Muhammad, K., Haq, I. U., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10619-3 · Cited by 16 · see citing works
Sahu, B., & Dash, S. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205668 · Cited by 15 · see citing works
DisepNet for breast abnormality recognition
Yu, X., Xia, K., & Zhang, Y. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106961 · Cited by 14 · see citing works
Modified student's t ‐hidden Markov model for pattern recognition and classification
Zhang, H., Wu, Q. M. J., & Nguyen, T. M. (2013) · IET Signal Processing · doi:10.1049/iet-spr.2012.0315 · Cited by 13 · see citing works
An Introduction to Quantum Computing
Ferry, D. K. (2020) · CRC Press eBooks · doi:10.4324/9781003031949-11 · Cited by 13 · see citing works
Combined Effect of Soft Computing Methods in Classification
Kompalli, V. S., & Usha Rani, K. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-2471-9_49 · Cited by 8 · see citing works
BIBHU: Biomarker Identification Using Bio-inspired Evolutionary Hybrid Unique Machine Learning Model
Sahu, B., & Dash, S. (2023) · doi:10.1109/wconf58270.2023.10235062 · Cited by 7 · see citing works
Adaboost algorithm with floating threshold
Fu, Z., Zhang, D., Li, X., et al. (2012) · doi:10.1049/cp.2012.0989 · Cited by 5 · see citing works
Feature Selection With Novel Mutual Information and Binary Grey Wolf Waterfall Model
Sahu, B., & Dash, S. (2023) · International Conference in Advances in Power, Signal, and Information Technology (APSIT) · doi:10.1109/apsit58554.2023.10201689 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dębowski, M., Kazimierowicz, J., & Zieliński, M. (2025) · Applied Sciences · doi:10.3390/app15137324 · Cited by 30 · see citing works
CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis
Chakraborty, C., Achar, U., Nayek, S., Achar, A., & Mukherjee, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-76852-6 · Cited by 12 · see citing works
- Authors
- Bilal, A., Imran, A., Baig, T. I., Liu, X., Abouel Nasr, E., & Long, H.
- Affiliations
- Hainan Normal University (CN)
- Air University (PK)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Long, Haixia
- Venue
- Scientific Reports
- Published
- 2024-05-10
- Volume
- 14
- Issue
- 1
- Pages
- 10714-10714
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11087531
- OpenAlex
- W4396805571
- Cited by
- 97
- References
- 72
- Citations by year
- 2026: 25 · 2025: 51 · 2024: 21
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Greater South Information System
- Funders
- Hainan Provincial Natural Science Foundation of China ((No.621RC1059, 621MS038, 823RC488, 724RC510, 721QN0890))
- King Saud University Researchers Supporting Program (RSP2024R164)
- National Natural Science Foundation of China (62262019)
- The Education Department of Hainan Province of China (No. Hnky2021-24)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- breast cancer diagnosis, computer-aided diagnosis, support vector machine, Gaussian kernel, hyperparameter optimization, feature selection, MIAS dataset, hybrid optimization algorithm
- MeSH terms
- Algorithms, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Mammography, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review