Inspired by the fundamentals of biological evolution, bio-inspired algorithms are becoming increasingly popular for developing robust optimization techniques. These metaheuristic algorithms, unlike gradient descent methods, are computationally more efficient and excel in handling higher order multi-dimensional and non-linear.
OBJECTIVES
To understand the hybrid Bio-inspired algorithms in the domain of Medical Imaging and its challenges of hybrid bio-inspired feature selection techniques.
METHOD
The primary research was conducted using the three major indexing database of Scopus, Web of Science and Google Scholar.
RESULT
The primary research included 198 articles, after removing the 103 duplicates, 95 articles remained as per the criteria. Finally 41 articles were selected for the study.
CONCLUSION
We recommend that further research in the area of bio-inspired algorithms based feature selection in the field of diagnostic imaging and clustering. Additionally, there is a need to further investigate the use of Deep Learning hybrid models integrating the bio-inspired algorithms to include the strengths of each models that enhances the overall hybrid model.
ManKFTangKSKwongS. Genetic algorithms: concepts and applications [in engineering design]. IEEE Transactions on Industrial Electronics.1996; 43(5): 519-534. doi: https://doi.org/10.1109/41.538609.
2.
BeniGWangJ. Swarm Intelligence in Cellular Robotic Systems. In: DarioPSandiniGAebischerP, editors. Robots and Biological Systems: Towards a New Bionics? Berlin, Heidelberg: Springer Berlin Heidelberg; 1993; pp. 703-712.
3.
KennedyJEberhartR. Particle swarm optimization. In: Proceedings of ICNN-95 – International Conference on Neural Networks. ICNN-95. IEEE; doi: https://doi.org/10.1109/ICNN.1995.488968.
4.
DorigoMDi CaroG. Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). CEC-99. IEEE;. doi: https://doi.org/10.1109/CEC.1999.782657.
BrasilLMde AzevedoFMBarretoJM. Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing. International Journal of Medical Informatics.2001 Sep; 63(1-2): 19-30. doi: https://doi.org/10.1016/S1386-5056(01)00168-X.
7.
SayedGIHassanienAEAzarAT. Feature selection via a novel chaotic crow search algorithm. Neural Computing and Applications.2017 Apr; 31(1): 171-188. doi: https://doi.org/10.1007/s00521-017-2988-6.
8.
SinghNSinghSB. A novel hybrid GWO-SCA approach for optimization problems. Engineering Science and Technology, an International Journal.2017 Dec; 20(6): 1586-1601. doi: https://doi.org/10.1016/j.jestch.2017.11.001.
9.
WoźniakMPołapD. Bio-inspired methods modeled for respiratory disease detection from medical images. Swarm and Evolutionary Computation.2018 Aug; 41: 69-96. doi: https://doi.org/10.1016/j.swevo.2018.01.008.
10.
Jude HemanthDAnithaJ. Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images. Applied Soft Computing.2019 Feb; 75: 21-28. doi: https://doi.org/10.1016/j.asoc.2018.10.054.
11.
ZemmalNAziziNSellamiMCherigueneSZianiAAlDwairiM, et al. Particle Swarm Optimization Based Swarm Intelligence for Active Learning Improvement: Application on Medical Data Classification. Cognitive Computation.2020 Aug; 12(5): 991-1010. doi: https://doi.org/10.1007/s12559-020-09739-z.
12.
PashaALathaPH. Bio-inspired dimensionality reduction for Parkinson’s disease (PD) classification. Health Information Science and Systems.2020 Mar; 8(1). doi: https://doi.org/10.1007/s13755-020-00104-w.
13.
VermaHVermaDTiwariPK. A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image. Expert Systems with Applications.2021 Apr; 167: 114121. doi: https://doi.org/10.1016/j.eswa.2020.114121.
14.
YousriDAbd ElazizMAbualigahLOlivaDAl-qanessMAAEweesAA. COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Applied Soft Computing.2021 Mar; 101: 107052. doi: https://doi.org/10.1016/j.asoc.2020.107052.
15.
ChakrabortyTBanikSKBhadraAKNandiD. Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images. Computer Methods and Programs in Biomedicine.2021 Apr; 202: 105971. doi: https://doi.org/10.1016/j.cmpb.2021.105971.
16.
SinghAKKumarAMahmudMKaiserMSKishoreA. COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier. Cognitive Computation.2021 Mar; doi: https://doi.org/10.1007/s12559-021-09848-3.
17.
PatilDOHamdeST. Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization. Multimedia Tools and Applications.2021 Feb; 80(12): 17955-17982. doi: https://doi.org/10.1007/s11042-020-10434-2.
18.
DixitAManiABansalR. CoV2-Detect-Net: Design of COVID-19 prediction model based on hybrid DE-PSO with SVM using chest X-ray images. Information Sciences.2021 Sep; 571: 676-692. doi: https://doi.org/10.1016/j.ins.2021.03.062.
19.
KoyuncuHBarstuğanM. COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier. Signal Processing: Image Communication.2021 Sep; 97: 116359. doi: https://doi.org/10.1016/j.image.2021.116359.
20.
PerumalVNarayananVRajasekarSJS. Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models. Computer Methods and Programs in Biomedicine.2021 Sep; 209: 106336. doi: https://doi.org/10.1016/j.cmpb.2021.106336.
21.
ZhaoSWangPHeidariAAChenHHeWXuS. Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy. Computers in Biology and Medicine.2021 Dec; 139: 105015. doi: https://doi.org/10.1016/j.compbiomed.2021.105015.
22.
DoeJSmithJRoeR. Enhancing the Practical Applicability of Genetic Algorithms by Optimizing Reproduction Operators. Computers in Biology and Medicine.2022; 146: 105383.
23.
GopatotiAVijayalakshmiP. CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer. Biomedical Signal Processing and Control.2022 Aug; 77: 103860. doi: https://doi.org/10.1016/j.bspc.2022.103860.
24.
BansalSRustagiAKumarA. Alzheimer’s disease diagnosis based on feature extraction using optimised crow search algorithm and deep learning. International Journal of Computer Applications in Technology.2021; 65(4): 325. doi: https://doi.org/10.1504/ijcat.2021.117272.
25.
KarunanayakeNLohitvisateWMakhanovSS. Artificial life for segmentation of fusion ultrasound images of breast abnormalities. Pattern Recognition.2022 Nov; 131: 108838. doi: https://doi.org/10.1016/j.patcog.2022.108838.
26.
KhalidAMHosnyKMMirjaliliS. COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle. Neural Computing and Applications.2022 Aug; 34(24): 22465-22492. doi: https://doi.org/10.1007/s00521-022-07639-x.
27.
SinghLKKhannaMThawkarSSinghR. Collaboration of features optimization techniques for the effective diagnosis of glaucoma in retinal fundus images. Advances in Engineering Software.2022 Nov; 173: 103283. doi: https://doi.org/10.1016/j.advengsoft.2022.103283.
28.
ShabanWM. Insight into breast cancer detection: new hybrid feature selection method. Neural Computing and Applications.2022 Dec; 35(9): 6831-6853. doi: https://doi.org/10.1007/s00521-022-08062-y.
29.
XingJZhouXZhaoHChenHHeidariAA. Elite levy spreading differential evolution via ABC shrink-wrap for multi-threshold segmentation of breast cancer images. Biomedical Signal Processing and Control.2023 Apr; 82: 104592. doi: https://doi.org/10.1016/j.bspc.2023.104592.
30.
DingHHuangQRodriguezD. Modified Locust Swarm optimizer for oral cancer diagnosis. Biomedical Signal Processing and Control.2023 May; 83: 104645. doi: https://doi.org/10.1016/j.bspc.2023.104645.
31.
RajeevSKPallikonda RajasekaranMVishnuvarthananGArunprasathT. A biologically-inspired hybrid deep learning approach for brain tumor classification from magnetic resonance imaging using improved gabor wavelet transform and Elmann-BiLSTM network. Biomedical Signal Processing and Control.2022 Sep; 78: 103949. doi: https://doi.org/10.1016/j.bspc.2022.103949.
32.
VijhSGauravPPandeyHM. Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection. Neural Computing and Applications.2020 Sep; 35(33): 23711-23724. doi: https://doi.org/10.1007/s00521-020-05362-z.
33.
Nadimi-ShahrakiMHZamaniHAsghari VarzanehZMirjaliliS. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. Archives of Computational Methods in Engineering.2023 May; 30(7): 4113-4159. doi: https://doi.org/10.1007/s11831-023-09928-7.
34.
BalasubramanianKN PA. Correlation-based feature selection using bio-inspired algorithms and optimized KELM classifier for glaucoma diagnosis. Applied Soft Computing.2022 Oct; 128: 109432. doi: https://doi.org/10.1016/j.asoc.2022.109432.
35.
Kumar SinghLKhannaMSinghR. A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction. Measurement.2023 Nov; 221: 113525. doi: https://doi.org/10.1016/j.measurement.2023.113525.
36.
PNNarayanS. Cardiac disease detection using cuckoo search enabled deep belief network. Intelligent Systems with Applications.2022 Nov; 16: 200131. doi: https://doi.org/10.1016/j.iswa.2022.200131.
37.
BraikMSHammouriAIAwadallahMAAl-BetarMAKhtatnehK. An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis. Biomedical Signal Processing and Control.2023 Aug; 85: 105073. doi: https://doi.org/10.1016/j.bspc.2023.105073.
38.
TiwariAChaturvediA. A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification. Expert Systems with Applications.2022 Jun; 196: 116621. doi: https://doi.org/10.1016/j.eswa.2022.116621.
39.
KilicarslanS, CelikM. Sahin Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification. Biomedical Signal Processing and Control. 2021 Jan; 63: 102231. doi: https://doi.org/10.1016/j.bspc.2020.102231.
40.
SurendarPBala PonniM. Diagnosis of lung cancer using hybrid deep neural network with adaptive sine cosine crow search algorithm. Journal of Computational Science.2021 Jul; 53: 101374. doi: https://doi.org/10.1016/j.jocs.2021.101374.
41.
PunithaSStephanTGandomiAH. A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections. Computer Methods and Programs in Biomedicine.2022 Feb; 214: 106432. doi: https://doi.org/10.1016/j.cmpb.2021.106432.
42.
AdamuAAbdullahiMJunaiduSBHassanIH. An hybrid particle swarm optimization with crow search algorithm for feature selection. Machine Learning with Applications.2021 Dec; 6: 100108. doi: https://doi.org/10.1016/j.mlwa.2021.100108.
43.
BaliarsinghSKDingWVipsitaSBakshiS. A memetic algorithm using emperor penguin and social engineering optimization for medical data classification. Applied Soft Computing.2019 Dec; 85: 105773. doi: https://doi.org/10.1016/j.asoc.2019.105773.
44.
LiuHGhadimiN. Hybrid convolutional neural network and Flexible Dwarf Mongoose Optimization Algorithm for strong kidney stone diagnosis. Biomedical Signal Processing and Control.2024 May; 91: 106024. doi: https://doi.org/10.1016/j.bspc.2024.106024.
45.
EmamMMHousseinEHSameeNAAlohaliMAHosneyME. Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved Coati optimization algorithm. Expert Systems with Applications.2024 Dec; 255: 124581. doi: https://doi.org/10.1016/j.eswa.2024.124581.
46.
MostafaRRKhedrAMAl AghbariZAfyouniIKamelIAhmedN. An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets. Knowledge-Based Systems.2024 Jan; 283: 111218. doi: https://doi.org/10.1016/j.knosys.2023.111218.
