A metaheuristic-based histogram equalization method for mammogram enhancement using a brightness preserving cuckoo search algorithm

A.M. Kamoona, J. Chandra Patra, A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images. Appl. Soft Comput. 85, 105749 (2019). https://doi.org/10.1016/j.asoc.2019.105749

Article  Google Scholar 

A. Ahmad, N.K. Verma, R.M. Aziz, Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm. J. Med. Syst. 1(1), 111 (2024). https://doi.org/10.1007/s10916-023-02031-1

Article  Google Scholar 

M. Alzaqebah, K. Briki, N. Al Refai, S. Brini, S. Jawarneh, M. Alsmadi, R. Mohammad, I. Almarashdeh, F. Alghamdi, N. Aldhafferi, A. Abdullah. Memory based cuckoo search algorithm for feature selection of gene expression dataset. Inform. Med. Unlocked. 24, 100572 (2021). https://doi.org/10.1016/j.imu.2021.100572

V. Atanasiu, I. Marthot-Santaniello, Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions. Int. J. Doc. Anal. Recogn. 25, 129–160 (2022). https://doi.org/10.1007/s10032-021-00386-0

Article  Google Scholar 

N.B. Bahadure, A.K. Ray, H. Thethi, Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging 2017, 1–12 (2017). https://doi.org/10.1155/2017/9749108

Article  Google Scholar 

P. Barthelemy, J. Bertolotti, A Lévy flight for light. Nature 453(7194), 495–498 (2008). https://doi.org/10.1038/nature06948

Article  ADS  Google Scholar 

B. Natarajan, SRS. Chakravarthy, VV. Kumar, B. Kavya, G. Meghana, H. Rajaguru. "Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis." Proceedings of the 15th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023), edited by A. Bajaj, A. Abraham, and O. Castillo, Lecture Notes in Networks and Systems, vol. 1243, Springer, 2025, pp. 44–56. https://doi.org/10.1007/978-3-031-81080-0_4.

C. Ummuhan, B. Alatas, Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems. Periodicals Eng. Natural Sci. 5(3), 403–409 (2017). https://doi.org/10.21533/pen.v5i3.120

Article  Google Scholar 

C. Tarun, Intuitionistic fuzzy approach for enhancement of low contrast mammogram images. Int. J. Imag. Syst. Technol. (2020). https://doi.org/10.1002/ima.22437

Article  Google Scholar 

E. Daniel, A. Jude, Optimum wavelet-based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm. Comput. Biol. Med. 71, 149–155 (2016). https://doi.org/10.1016/j.compbiomed.2016.02.011

Article  Google Scholar 

D.K. Patra, T. Si, S. Mondal, A. Das, P.K. Nanda, S. Sweta, V. Tripathy, Breast lesion detection from MRI images using quasi-oppositional slime mould algorithm. Multimed. Tools Appl. 82, 30599–30641 (2023). https://doi.org/10.1007/s11042-023-14329-w

Article  Google Scholar 

D.K. Patra, T. Si, S. Mondal, A. Das, P.K. Nanda, S. Sweta, V. Tripathy, Magnetic resonance image of breast segmentation by multi-level thresholding using moth-flame optimization and whale optimization algorithms. Pattern Recognition Image Anal. 32, 174–186 (2022). https://doi.org/10.1134/S1054661822010060

Article  Google Scholar 

U. Haris, V. Kabeer, K. Afsal, Breast Cancer Segmentation Using Hybrid HHO-CS SVM Optimization Techniques. Multimed. Tools Appl. 83, 69145–69167 (2024). https://doi.org/10.1007/s11042-023-18025-0

Article  Google Scholar 

Heath, Michael, Kevin W. Bowyer, Daniel Kopans, Richard Moore, and Philip Kegelmeyer. "Current Status of the Digital Database for Screening Mammography." Digital Mammography, edited by Nico Karssemeijer, Martin Thijssen, Jos Hendriks, and Luc van Erning, Computational Imaging and Vision, vol. 13, Springer, 1998, pp. 749–761. https://doi.org/10.1007/978-94-011-5318-8_75.

S. Iniyan, M.S. Raja, R. Poonguzhali, A. Srinivasan, R. Keerthika, Enhanced breast cancer diagnosis through integration of computer vision with fusion-based joint transfer learning using multi-modality medical images. Sci. Rep. 14, 28376 (2024). https://doi.org/10.1038/s41598-024-79363-6

Article  Google Scholar 

A. Jabeen, M.M. Riaz, N. Iltaf, A. Ghafoor, Image contrast enhancement using weighted transformation function. IEEE Sensors J. 16(20), 7534–7536 (2016)

ADS  Google Scholar 

A.N. Karahaliou, I.S. Skiadopoulos, S.G. Skiadopoulos, F.N. Sakellaropoulos, N.S. Arikidis, E.A. Likaki, G.S. Panayiotakis, L.I. Costaridou, Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf. Technol. Biomed. 12(6), 731–738 (2008)

Google Scholar 

N. Kavitha, P. Madhumathy, R.M. Prasad, Machine learning technique for breast cancer detection and classification. Mach. Learn. Comput. Sci. Eng. 1, 16 (2025). https://doi.org/10.1007/s44379-025-00018-y

Article  Google Scholar 

K. Dhal Gopal, S. Das, Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. Pattern Recognition Image Anal. 27(4), 695–712 (2017). https://doi.org/10.1134/S1054661817040046

Article  Google Scholar 

P.J.S. Kumar, S. Shibu, M. Mohan, T. Kalaichelvi, Hybrid deep learning enabled breast cancer detection using mammogram images. Biomed. Signal Process. Control 95(Part A), 106310 (2024). https://doi.org/10.1016/j.bspc.2024.106310

Article  Google Scholar 

V. Kusla, G.S. Brar, H. Kaur et al., Chameleon swarm algorithm with morlet wavelet mutation for superior optimization performance. Sci. Rep. 15, 13971 (2025). https://doi.org/10.1038/s41598-025-97015-1

Article  Google Scholar 

K. Li, K. Deb, Q. Zhang, S. Kwong, An evolutionary many objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2014)

Google Scholar 

H. Li, M.L. Giger, O.I. Olopade, A. Margolis, Li. Lan, M.R. Chinander, Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad. Radiol. 12(7), 863–873 (2005)

Google Scholar 

H.H. Luong, M.D. Vo, H.P. Phan et al., Improving breast cancer prediction via progressive ensemble and image enhancement. Multimed. Tools Appl. 84, 8623–8650 (2025). https://doi.org/10.1007/s11042-024-19299-1

Article  Google Scholar 

R. Majji, G. Gopal, O. Rajeswari et al., Smart IoT in breast cancer detection using optimal deep learning. J. Digit. Imaging 36, 1489–1506 (2023). https://doi.org/10.1007/s10278-023-00834-9

Article  Google Scholar 

M. Malik, F. Ahsan, S. Mohsin, Adaptive image denoising using cuckoo algorithm. Soft. Comput. 20(3), 925–938 (2016). https://doi.org/10.1007/s00500-014-1552-x

Article  Google Scholar 

S.N. Makhadmeh, M.A. Awadallah, S. Kassaymeh et al., Recent advances in multi-objective cuckoo search algorithm, its variants and applications. Archiv. Comput. Methods in Eng. (2025). https://doi.org/10.1007/s11831-025-10240-9

Article  Google Scholar 

B. Masoudi, H.S. Aghdasi, An Image Segmentation Method Based on Improved Monarch Butterfly Optimization. Iranian Journal of Computer Science 5, 41–54 (2022). https://doi.org/10.1007/s42044-021-00084-4

Article  Google Scholar 

M. Masotti, N. Lanconelli, R. Campanini, Computer-Aided Mass Detection in Mammography: False Positive Reduction via Gray-Scale Invariant Ranklet Texture Features. Med. Phys. 36, 311–320 (2009)

Google Scholar 

M.M. Mehdy, P.Y. Ng, E.F. Shair, N.I. Md Saleh, C. Gomes, Artificial neural networks in image processing for early detection of breast cancer. Comput. Math. Methods Med. 2017, 15 (2017). https://doi.org/10.1155/2017/2610628

Article  Google Scholar 

F. Mohanty, S. Rup, B. Dash, B. Majhi, M. Nagarajan Swamy, Mammogram classification using contourlet features with forest optimization-based feature selection approach. Multimed. Tools Appl. 78(10), 12805–12834 (2019)

Google Scholar 

M. Noor, Nor’ain S. M. Sahidi, H. Yazid, K. S. A. Rahman, and S. Daud. "Contrast Enhancement Method Using Partial Contrast Technique on Breast Cancer Histopathology Images." In: 6th International Conference on Biomedical Engineering (ICOBE 2023), edited by H. L. Lee, H. Yazid, and F. Ibrahim, IFMBE Proceedings, vol. 115, Springer, , pp. 105–116.,(2025) https://doi.org/10.1007/978-3-031-80355-0_12.

Natarajan, Rajesh, S. Krishna, H. L. Gururaj, and et al. "A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction." International Journal of Computational Intelligence Systems, vol. 18, article 7, (2025). https://doi.org/10.1007/s44196-024-00712-4.

D. Nie, L. Wang, E. Adeli et al., 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation. IEEE Trans. Cybernetics 49, 1123–1136 (2019). https://doi.org/10.1109/tcyb.2018.2797905

Article  Google Scholar 

F.L.S. Numes, H. Schiabel, C.E. Goes, Contrast enhancement in dense breast images to aid clustered microcalcifications detections. J. Digital Imag. 20(1), 53–66 (2007)

Google Scholar 

R. Pal, P. Roy, S. Mallick, S. Mukhopadhyay, S. Sarkar, M. Hinchey, A Multi-Objective Cuckoo Search Algorithm Using Generalized Lévy Flight and Dissimilar Egg Identification for Multispectral Image Thresholding. Appl. Soft Comput. 175, 113054 (2025). https://doi.org/10.1016/j.asoc.2025.113054

Article  Google Scholar 

S. Panigrahi, H. Swapnarekha, S. Subudhi. "GACO: A Genetic Algorithm with Ant Colony Optimization-Based Feature Selection for Breast Cancer Diagnosis." In Nature-Inspired Optimization Methodologies in Biomedical and Healthcare, edited by Jyotirmay Nayak, Ajith K. Das, B. Naresh Kumar Meher, and Saikat Brahnam, Intelligent Systems Reference Library, vol. 233, Springer, , pp. 157–174., (2023) https://doi.org/10.1007/978-3-031-17544-2_12.

A.S. Parihar, O.P. Verma, Contrast enhancement using entropy-based dynamic sub-histogram equalisation. IET Image Process. 10(11), 799–808 (2016)

Google Scholar 

I. Pavlyukevich, Lévy Flights, Non-Local Search and Simulated Annealing. J. Comput. Phys. 226(2), 1830–1844 (2007)

ADS  MathSciNet  Google Scholar 

P. Bhalerao, S.V. Bonde, Cuckoo search-based multi-objective algorithm with decomposition for detection of masses in mammogram images. Int. J. Inform. Technol. 13, 2215–2226 (2021). https://doi.org/10.1007/s41870-021-00805-9

Article  Google Scholar 

A. Prakash, A.K. Bhandari, Cuckoo search constrained gamma masking for MRI image contrast enhancement. Multimedia Tools and Applications 82, 40129–40148 (2023). https://doi.org/10.1007/s11042-023-14545-4

Article  Google Scholar 

A.M. Reynolds, M.A. Frye, Free-flight odor tracking in drosophila is consistent with an optimal intermittent scale-free search. PLoS ONE 2(4), e354 (2007)

ADS  Google Scholar 

J. Robertson, P. Kirkland, J.A. Alanis, et al. Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci. Rep. 12, 4874 (2022). https://doi.org/10.1038/s41598-022-08703-1

Article  ADS  Google Scholar 

P. Sahni, N. Mittal, Breast cancer detection using image processing techniques (2019). https://doi.org/10.1007/978-981-13-6577-5_79

M. Sameti, R.K. Ward, J. Morgan-Parkes, B. Palcic, Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE J. Select. Topics Signal Process. 3(1), 46–52 (2009)

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